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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ

Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Поддерживает ввод нескольких поисковых фраз (по одной на строку). При поиске обеспечивает поддержку морфологии русского и английского языка
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Применить Всего найдено 9340. Отображено 100.
23-02-2012 дата публикации

Image analyis method and system

Номер: US20120045106A1
Автор: Bengt Langstrom, Pasha Razifar
Принадлежит: Individual

The invention relates to a system and method for enhancing image data obtained from a positron emission tomography (PET) scan. In various embodiments, the method comprises transforming an original image data set to provide a first modified image data set by performing a masked volume-wise principal component analysis (MVW-PCA) on the original image data set. The first modified image data set is then transformed to provide a second modified image data set by performing a masked volume-wise independent component analysis (MVW-ICA) on the first modified image data set, the second modified image data set thereby comprising enhanced image data.

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Номер записи: 1
01-03-2012 дата публикации

Medical image alignment apparatus, method, and program

Номер: US20120053454A1
Автор: Caihua WANG, Keigo Nakamura, Satoshi Ihara
Принадлежит: Fujifilm Corp

Generating, with respect to each of the three-dimensional image and the three-dimensional comparison image, a plurality of tomographic images orthogonal to a central axis of each vertebra of the subject along the central axis, calculating a first characteristic amount representing a profile in a direction orthogonal to the central axis at each point on the central axis based on the tomographic images, calculating a second characteristic amount representing a profile in a direction of the central axis at each point on the central axis based on the tomographic images, calculating a third characteristic amount representing regularity of disposition of each vertebra at each point on the central axis based on the calculated first and second characteristic amounts, and aligning positions of the third characteristic amount calculated from the three-dimensional image and the third characteristic amount calculated from the three-dimensional comparison image along the central axis.

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Номер записи: 2
01-03-2012 дата публикации

Method and System For Patient-Specific Modeling of Blood Flow

Номер: US20120053921A1
Автор: Charles A. Taylor
Принадлежит: HeartFlow Inc

Embodiments include a system for planning treatment for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of an anatomical structure of the patient, create a three-dimensional model representing at least a portion of the anatomical structure of the patient based on the patient-specific data, and determine a first fractional flow reserve within the anatomical structure of the patient based on the three-dimensional model and information regarding a physiological condition of the patient. The at least one computer system may be further configured to receive input from a user regarding a plan of treatment, modify the physiological condition of the patient based on the received input, and determine a second fractional flow reserve within the anatomical structure of the patient based on the modified physiological condition of the patient.

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Номер записи: 3
17-05-2012 дата публикации

Method for optical pose detection

Номер: US20120121124A1
Автор: Christoph Forman, Murat AKSOY, Roland Bammer
Принадлежит: Leland Stanford Junior University

The tracking and compensation of patient motion during a magnetic resonance imaging (MRI) acquisition is an unsolved problem. A self-encoded marker where each feature on the pattern is augmented with a 2-D barcode is provided. Hence, the marker can be tracked even if it is not completely visible in the camera image. Furthermore, it offers considerable advantages over a simple checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant. Significantly improved accuracy relative to a planar checkerboard pattern is obtained for both phantom experiments and in-vivo experiments with substantial patient motion. In an alternative aspect, a marker having non-coplanar features can be employed to provide improved motion tracking. Such a marker provides depth cues that can be exploited to improve motion tracking. The aspects of non-coplanar patterns and self-encoded patterns can be practiced independently or in combination.

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Номер записи: 4
02-08-2012 дата публикации

Systems and methods for matching, naming, and displaying medical images

Номер: US20120194540A1
Автор: Evan K. Fram, John J. Schumacher, Murray A. Reicher, Steven M. Greim
Принадлежит: DR Systems Inc

A method of matching medical images according to user-defined matches rules. In one embodiment, the matched medical images are displayed according user-defined display rules such that the matched medical images may be visually compared in manner that is suitable to the viewer's viewing preferences.

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Номер записи: 5
30-08-2012 дата публикации

Method for quantifying the development of pathologies involving changes in the volumes of bodies, notably tumors"

Номер: US20120220856A1
Автор: Elsa Angelini, Emmanuel Mandonnet, Julie Delon
Принадлежит: CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS, Telecom ParisTech, Universite Paris Diderot Paris 7

A method for quantifying the development of pathologies involving changes in volume of a body represented via an imaging technique, including normalizing gray levels by a midway technique for two images I 1 and I 2 representing the same scene, resulting in two normalized images I′ 1 and I′ 2 ; calculating a map of signed differences between the two normalized images I′ 1 and I′ 2 ; and performing one or more statistical tests based on the assumption of a Gaussian distribution of the gray levels for healthy tissues in the normalized images I′ 1 and I′ 2 and/or in the calculated difference map. Advantageously, results of two or more of the tests can be combined for a more specific characterization of the development.

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Номер записи: 6
11-10-2012 дата публикации

Magnetic resonance system and method to generate diffusion information

Номер: US20120259199A1
Автор: David Andrew Porter, Stefan Huwer
Принадлежит: Individual

In a magnetic resonance (MR) method and system for the generation of diffusion information, diffusion-weighted MR images of an examination subject are generated, each image being generated using an individual diffusion gradient. The diffusion gradients, and therefore the MR images, are sorted such that, after the sorting, a predefined number of diffusion gradients respectively forms a group. Each diffusion gradient belongs to at least one of these groups, and the diffusion gradients of the respective same group are all as linearly independent of one another as possible. The MR images whose diffusion gradients form a group are assembled into an MR result image. Spatial transformations between the MR result images are determined, and the MR images are modified using these spatial transformations. The diffusion information is formed with the aid of the modified MR images.

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Номер записи: 7
08-11-2012 дата публикации

System and method for automatic recognition and labeling of anatomical structures and vessels in medical imaging scans

Номер: US20120281904A1
Автор: Giridharan R. Iyengar, Jonathan B. Rohrer, Leiguang Gong
Принадлежит: International Business Machines Corp

A system and method for recognizing and labeling anatomical structures in an image includes creating a list of objects such that one or more objects on the list appear before a target object and setting the image as a context for a first object on the list. The first object is detected and labeled by subtracting a background of the image. A local context is set for a next object on the list using the first object. The next object is detected and labeled by registration using the local context. Setting a local context and detecting and labeling the next object are repeated until the target object is detected and labeled. Labeling of the target object is refined using region growing.

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Номер записи: 8
20-12-2012 дата публикации

Methods and apparatus for assessing activity of an organ and uses thereof

Номер: US20120323108A1
Автор: Robert G. Carroll
Принадлежит: QUANTITATIVE IMAGING Inc

Methods and apparatus are provided for imaging activity of an organ of a subject for diagnosis and prognosis of pathology or injury to the organ, where unaffected portions of the organ are used as a reference for assessing activity of afflicted areas of the organ.

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Номер записи: 9
14-03-2013 дата публикации

Method and system for patient-specific modeling of blood flow

Номер: US20130064438A1
Автор: Charles A. Taylor, Christopher K. Zarins, Gilwoo Choi, Timothy A. Fonte, Ying Bai
Принадлежит: HeartFlow Inc

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

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Номер записи: 10
14-03-2013 дата публикации

Normative dataset for neuropsychiatric disorders

Номер: US20130066189A1
Автор: Dieter Geller, Juergen Weese, Lyubomir Georgiev ZAGORCHEV, Matthew A. GARLINGHOUSE, Reinhard Kneser, Robert M. Roth, Thomas W. Mcallister, Yuechen Qian
Принадлежит: KONINKLIJKE PHILIPS ELECTRONICS NV

A system and method for identifying an abnormality of an anatomical structure. The system and method segments, using a processor, the anatomical structure imaged in a volumetric image of a plurality of control patients to produce a control segmentation of the anatomical structures of each of the control patients, obtains a normative dataset by extracting a statistical representation of a morphology of the control segmentations, segments the anatomical structure of a patient being analyzed for abnormalities to produce a patient segmentation and compares the patient segmentation to the normative dataset obtained from the control segmentations.

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Номер записи: 11
14-03-2013 дата публикации

Method and system for patient-specific modeling of blood flow

Номер: US20130066618A1
Автор: Charles A. Taylor, Christopher K. Zarins, Gilwoo Choi, Timothy A. Fonte, Ying Bai
Принадлежит: HeartFlow Inc

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

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Номер записи: 12
30-05-2013 дата публикации

Method and system for automatically setting a landmark for brain scans

Номер: US20130136329A1
Автор: Robert David Darrow, Thomas Kwok-Fah Foo, Vivek Prabhakar Vaidya, Xiaodong Tao
Принадлежит: General Electric Co

A method, system and apparatus for automatically setting a landmark for brain scans are described. In one embodiment, a method for medical image processing is described. The method comprises obtaining, by an imaging device, at least one image of a head of a subject. In addition, the method also comprises identifying, by a computer-based system, a reference feature in the at least one image associated with the head. The method also comprises automatically setting, by the computer-based system, a landmark based, at least in part, upon the reference feature.

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Номер записи: 13
27-06-2013 дата публикации

Computing the mass of an object

Номер: US20130163836A1
Автор: Daniele Masala, Danilo Pietro Pau, Xinfeng Bao
Принадлежит: STMICROELECTRONICS SRL

The mass of an object may be estimated based on intersection points of a representation of a surface in an image space with cubes defining the image space, the surface representing a surface of an object. The representation may be, for example, based on marching cubes. The mass may be estimated by estimating a mass contribution of a first set of cubes contained entirely within the representation of the surface, estimating a mass contribution of a second set of cubes having intersection points with the representation of the surface, and summing the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes. The object may be segmented from other portions of an image prior to estimating the mass of the object.

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Номер записи: 14
11-07-2013 дата публикации

Method for registering function mri data

Номер: US20130177228A1
Автор: Di Jiang, Tianzi Jiang, Yong Fan
Принадлежит: Shenyang Institute of Automation of CAS

A method for registering functional MRI data, comprising: computing the functional connectivity pattern for every voxel in its given spatial neighborhood for every fMRI image; extracting features invariant to spatial location of the neighboring voxels based on the functional connectivity patterns; constructing similarity metric between voxels of different images based on the extracted features, and using fluid-like demons registration model to spatial normalize the fMRI data. The present invention tries to exploit the multi-range functional connectivity information of the fMRI data, and to register functional MR images based on the extracted spatial-location-invariant features. The present invention is robust against local spatial perturbations and does not depend on the assumption that functional signals of different subjects are synchronic, hence can be applied to resting-state fMRI data, and can achieve a statistically significant improvement in functional consistency across subjects.

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Номер записи: 15
08-08-2013 дата публикации

Method and a system for multi-dimensional visualization of the spinal column by vertebra vectors, sacrum vector, sacrum plateau vector and pelvis vector

Номер: US20130202179A1
Автор: Szabolcs Somoskeöy, Tamas Illes
Принадлежит: PÉCSI TUDOMÁNYEGYETEM

A new system and method introducing vertebra vectors for the three-dimensional (3D) visualization and its use for the complete 3D characterization and numerical analysis of vertebrae in spinal deformities is covered in this application. A vertebra vector representing its respective vertebra means a simplification of the very complex visual information in digital images and 3D reconstructions provided by the current radiodiagnostic devices, without sacrificing relevant data for the information important to understand the underlying processes in spinal deformation: 3D mathematical data on vertebral size, position, orientation and rotation. A series of vertebra vectors of the thoracic and lumbar spinal region provide the ability of a virtual 3D reconstruction of the spine, in frontal, sagittal and horizontal plane. Conventional angulation measurement methods to describe and characterize the spinal column in the frontal and sagittal plane are preserved and readily applicable by methods using vertebra vectors as well.

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Номер записи: 16
15-08-2013 дата публикации

Method and system for modeling and processing fmri image data using a bag-of-words approach

Номер: US20130211229A1
Автор: Berkan Solmaz, Mubarak Shah, Ravishankar Rao, Soumyabrata Dey
Принадлежит: International Business Machines Corp, UNIVERSITY OF CENTRAL FLORIDA

Systems and methods for processing image data are provided. A computer implemented method for processing image data, comprises gathering 4-D image data from a subject, extracting time series data, and spatial and degree data of each voxel of the subject, deriving at least one feature from the time series data, deriving at least one feature from the spatial and degree data, combining the at least one feature from the time series data, and the at least one feature from the spatial and degree data to generate combined data, and inputting the combined data to a classifier, wherein the classifier outputs a classification based on the combined data.

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Номер записи: 17
29-08-2013 дата публикации

Methods for quantitative assessment of volumetric image from a subject and uses therof

Номер: US20130223714A1
Автор: Michael L. Lipton, Namhee Kim
Принадлежит: ALBERT EINSTEIN COLLEGE OF MEDICINE

Methods and systems are disclosed for assessing a quantitative image volume from an individual subject comprising comparing the image from the subject to images from a control group of subjects using voxel-wise comparison. The methods allow detection of pathologies or lesions in the individual subject being assessed.

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Номер записи: 18
17-10-2013 дата публикации

Method and apparatus for orienting image representative data

Номер: US20130272588A1
Автор: George E. Ketcher, Jeanne K. Hertz, Jerold N. Luisi, Timothy R. Littlefield
Принадлежит: Individual

A method for processing a three-dimensional image file captured directly from a live subject, the file including the cranium of the subject, comprises: providing a vertex point cloud for the three-dimensional image file; determining a median point for the vertex point cloud; determining a point on the cranium; and utilizing the median point and the cranium point to define a z-axis for the three-dimensional image file.

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Номер записи: 19
31-10-2013 дата публикации

Automatic quantification of asymmetry

Номер: US20130289395A1
Автор: Frank Olaf Thiele
Принадлежит: KONINKLIJKE PHILIPS ELECTRONICS NV

An apparatus detects asymmetry in an object, such as a brain. The apparatus includes a processor programmed to fit a three-dimensional image of the object to a preselected shape, such as a standard brain atlas. The processor projects the three-dimensional image of the object to a two-dimensional surface image. The processor compares corresponding mirror image symmetric voxel pairs on the left and right sides of the surface image. The processor generates at least one of an asymmetry map and an asymmetry index based on the deviations in the pixel pairs. The processor can also mask, before the comparison, pixels of the surface image which are asymmetric in a normal brain.

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Номер записи: 20
14-11-2013 дата публикации

Method and apparatus for processing of stroke ct scans

Номер: US20130303900A1
Автор: Wieslaw Lucjan Nowinski
Принадлежит: Agency for Science Technology and Research Singapore

An automatic technique for stroke identification, localization, quantification and prediction, has the steps of receiving a CT scan, pre-processing it to extract the brain region corresponding to a brain volume of a subject who has suffered a stroke; identifying whether a hemorrhage is present in the brain volume and if so obtaining data characterizing the hemorrhage; otherwise identifying whether an infarct is present and if so obtaining data characterizing it; analyzing the results using a brain atlas; and, using the results of the analysis, obtaining at least one predictive value characterizing a prediction about the subject.

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Номер записи: 21
21-11-2013 дата публикации

Methods, systems and devices for a clinical data reporting and surgical navigation

Номер: US20130307955A1
Автор: Adam Deitz, Richard K. Grant
Принадлежит: Ortho Kinematics Inc

Three components are proposed, each having at its core a system for producing measurements of the relative motion of anatomical structures of mammals (the “measurement system”). The measurement system in this case would be comprised of an apparatus for imaging the joint of through a prescribed motion, and a process and mechanism for deriving quantitative measurement output data from the resulting images. The components of the present invention include: (1) a software device for reporting measurement output of the measurement system and for allowing users to interact with the measurement output data; (2) an apparatus and method for utilizing measurement output of the measurement system for therapeutic and surgical applications such as surgical navigation and patient positioning during a therapeutic procedure; and (3) an apparatus providing input image data for the measurement system that assists with the imaging of joints connecting anatomical regions that are in motion during operation.

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Номер записи: 22
28-11-2013 дата публикации

Methods and Apparatus for Estimating Clinical Measures

Номер: US20130315460A1
Автор: Marcel Warntjes
Принадлежит: SyntheticMR AB

In a magnetic resonance imaging display system, the brain parenchymal fraction, a clinical measure for brain atrophy, is found by selection of white matter, grey matter, and/or cerebrospinal fluid based on quantitative magnetic resonance properties.

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Номер записи: 23
07-01-2021 дата публикации

NEUROLOGICAL EXAMINATION SYSTEM

Номер: US20210000350A1
Автор: HAAF JOEL, Zagorchev Lyubomir Georgiev
Принадлежит:

Systems and methods for evaluating an anatomical structure in a brain of a subject are provided. In an embodiment, a system for evaluating an anatomical structure in a brain of a subject includes a computing device in communication with a magnetic resonance imaging (MRI) device. The computing device operable to determine an abnormality in the anatomical structure by comparing a test activation level within a geometry of the anatomical structure to data in a normative database, and output, to a display device, a graphical representation of the abnormality in the anatomical structure. The test activation level is determined by aligning functional magnetic resonance imaging (fMRI) data obtained by use of the MRI device and the geometry of the anatomical structure. The geometry of the anatomical structure is delineated based on segmentation of magnetic resonance (MR) data obtained by use of the MRI device. The data in the normative database include activation levels of the anatomical structure of a plurality of neurologically non-diseased subjects.

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Номер записи: 24
04-01-2018 дата публикации

Image processing apparatus, image processing method, and program

Номер: US20180000440A1
Автор: Kazuhiro Nishikawa
Принадлежит: Nihon Medi Physics Co Ltd

An image processing device includes: an image data acquisition unit for acquiring SPECT image data of a brain; a brain-region ROI definition unit for defining a brain-region ROI in the SPECT image; a striatum ROI definition unit for defining a striatum ROI in the SPECT image; and a threshold determination unit for, based on counts in the SPECT image's background which is the brain-region ROI except the striatum ROI, determining a threshold for distinguishing ventricles and sulci in the SPECT image; a region distinction unit for distinguishing between a region whose number of counts is smaller than or equal to the threshold and a region whose number of counts is larger than the threshold.

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Номер записи: 25
05-01-2017 дата публикации

Device and method for spatiotemporal reconstruction of a moving vascular pulse wave in the brain and other organs

Номер: US20170000441A1
Автор: William E. Butler
Принадлежит: Individual

The brain appears to have organized cardiac frequency angiographic phenomena with such coherence as to qualify as vascular pulse waves. Separate arterial and venous vascular pulse waves may be resolved. This disclosure states the method of extracting a spatiotemporal reconstruction of the cardiac frequency phenomena present in an angiogram obtained at faster than cardiac frequency. A wavelet transform is applied to each of the pixel-wise time signals of the angiogram. If there is motion alias then instead a high frequency resolution wavelet transform of the overall angiographic time intensity curve is cross-correlated to high temporal resolution wavelet transforms of the pixel-wise time signals. The result is filtered for cardiac wavelet scale then pixel-wise inverse wavelet transformed. This gives a complex-valued spatiotemporal grid of cardiac frequency angiographic phenomena. It may be rendered with a brightness-hue color model or subjected to further analysis.

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Номер записи: 26
07-01-2016 дата публикации

SYSTEM AND METHOD FOR DETERMINING TARGET STIMULATION VOLUMES

Номер: US20160001080A9
Автор: BARNHORST Jordan, BLUM David Arthur, CARLTON Keith, Kokones Scott, Moffitt Michael A.
Принадлежит: BOSTON SCIENTIFIC NEUROMODULATION CORPORATION

A system and method may include determining a target stimulation volume based on modifying a patient population image for which an efficacious volume had been determined. A system and method for suggesting stimulation devices may include determining which stimulation device is capable of producing an output volume of activation that most closely matches the target volume. A system and method for facilitating selection of stimulation parameters may include graphically identifying a maximum volume in which tissue is stimulatable by an implanted stimulation device. A system and method may pre-compute volumes of activation that result from a predetermined modification of programming settings. A system and method may transmit stimulation programming settings from a stimulation programming module to a stimulation generating device. 15-. (canceled)6. The method of claim 14 , wherein the anatomical region of the at least one selected patient is a composite anatomical region corresponding to the plurality of selected patients.78-. (canceled)9. A computer-implemented method for determining a stimulation volume for a subject patient claim 14 , the method comprising:associating, by a computer processor, anatomical features in a first image, corresponding to an anatomical region of the subject patient, with corresponding anatomical features in a second image, corresponding to an anatomical region of at least one selected patient from a patient population, wherein a stimulation volume has been predetermined for the at least one selected patient;modifying, by the processor, the second image so that spatial relationships of the anatomical features in the second image match spatial relationships of the anatomical features in the first image; anddetermining one of a target stimulation volume and a side effect stimulation volume based on the modifying.10. The method of claim 9 , wherein the target stimulation volume is determined by modifying the predetermined stimulation volume along ...

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Номер записи: 27
05-01-2017 дата публикации

SYSTEM AND METHOD FOR GENERATING MAGNETIC RESONANCE IMAGING (MRI) IMAGES USING STRUCTURES OF THE IMAGES

Номер: US20170003366A1
Автор: Jafari-Lhouzani Kourosh
Принадлежит:

A system and method for generating high resolution (HR) images from low resolution (LR) images or data by selectively choosing neighbors and the tissue types of the neighbors when estimating the image intensity of a voxel with the values of the neighbors. The system and method may interpolate LR images of a first contrast with the help of the high resolution HR images of a second contrast using the anatomical structures in both sets of images. 1. A method for generating magnetic resonance imaging (MRI) images of a subject , the steps of the method comprising:a) acquiring low resolution (LR) MRI images of a first contrast and high resolution (HR) MRI images of a second contrast;b) generating HR images of the first contrast by interpolating the LR MRI images of the first contrast to desired resolutions with a first interpolation method;c) coregistering the HR images of the second contrast with the HR MRI images of the first contrast to generate coregistered HR images of the second contrast and coregistered HR images of the first contrast;d) estimating first weights using laminar structures of the coregistered HR images of the second contrast;e) generating first interpolated HR images of the first contrast using the first weights and the coregistered HR images of the first contrast;f) estimating second weights based on the first interpolated HR images of the first contrast and the coregistered HR images of the second contrast using laminar structures of the first interpolated HR images of the first contrast and the laminar structures of the coregistered HR images of the second contrast; andg) generating final interpolated HR images of the first contrast using the second weights and the first interpolated HR images of the first contrast2. The method as recited in claim 1 , whereinstep d) further comprisingi) detecting first edges of the coregistered HR images of the second contrast;ii) propagating first edge information of the coregistered HR images of the second ...

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Номер записи: 28
05-01-2017 дата публикации

COMPRESSIVE PLENOPTIC MICROSCOPY

Номер: US20170003491A1
Автор: Adesnik Hillel, Pegard Nicolas, Waller Laura
Принадлежит: The Regents of the University of California

A system and method for quantitative functional neuroimaging through thick brain tissue in live animals. A computational imaging method is disclosed that uses plenoptic image acquisition including a first initialization step that identifies individual neurons by their optical signature and provides a reliable estimate of their position in space and a second stimulation-based image processing step that used acquired calibration data to quickly quantify activity in each identified neuron at video frame-rate. 1. A functional imaging apparatus , comprising:(a) a computer processor; and(b) a memory storing instructions executable by the computer processor; (i) acquiring initial image data from a sample tissue, the initial image data comprising features corresponding to a plurality of neurons within the sample tissue;', '(ii) generating a database of individual optical signatures, each of the individual optical signatures corresponding to an identified neuron within the plurality of neurons;', '(iii) acquiring secondary image data from the sample tissue, the secondary image data comprising features corresponding to a plurality of neurons responsive to an external stimulus applied to the sample tissue; and', '(iv) decomposing the secondary image data as a function of the database of individual optical signatures to output a quantitative measure of fluorescence levels of the identified individual neurons., '(c) said instructions, when executed by the computer processor, performing steps comprising2. The apparatus of claim 1 , wherein the database comprises data corresponding to an estimated location in space for each identified neuron within the plurality of neurons.3. The apparatus of claim 1 , wherein the secondary image data is decomposed as a function of a positive claim 1 , linear combination of one or more image frames within database of individual optical signatures.4. The apparatus of :wherein acquiring initial image data comprises acquiring video data from the ...

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Номер записи: 29
07-01-2016 дата публикации

MICROSCOPY IMAGING DEVICE WITH ADVANCED IMAGING PROPERTIES

Номер: US20160004063A1
Автор: Burns Laurie, Cocker Eric, El Gamal Abbas, Ghosh Kunal, Ho Tatt Wei, Schnitzer Mark J.
Принадлежит:

Systems, methods and devices are implemented for microscope imaging solutions. One embodiment of the present disclosure is directed toward an epifluorescence microscope. The microscope includes an image capture circuit including an array of optical sensor. An optical arrangement is configured to direct excitation light of less than about 1 mW to a target object in a field of view of that is at least 0.5 mmand to direct epi-fluorescence emission caused by the excitation light to the array of optical sensors. The optical arrangement and array of optical sensors are each sufficiently close to the target object to provide at least 2.5 μm resolution for an image of the field of view. 1. An epifluorescence microscopy system , the system comprising:an optical excitation arrangement configured to direct light over an area encompassed within a field of view containing an imaging target along a specimen plane;an imaging circuit including an optical sensor array configured to generate image data from fluorescence caused by an interaction between the directed light and the imaging target;a synchronization circuit in communication with the imaging circuit and configured to interface with an external optical-data processing system that provides visual feedback of the image data; andan optical arrangement configured to direct the fluorescence through a dichroic mirror to the optical sensor array with sufficient intensity and focus for the image data to reach cellular level brain imaging,wherein the epifluorescence microscopy system is configured to be attached and reattached to a base plate of a supportive structure for allowing precise alignment of the epifluorescence microscopy system for repeated imaging of the field of view containing the imaging target during chronic experiments, andwherein the epifluorescence microscopy system has a dimension parallel to the specimen plane that does not exceed 1 inch.2. The epifluorescence microscopy system of claim 1 , wherein the optical ...

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Номер записи: 30
07-01-2016 дата публикации

DETERMINATION OF NEUROPSYCHIATRIC THERAPY MECHANISMS OF ACTION

Номер: US20160004821A1
Автор: ANGELL ROBERT L., FRIEDLANDER ROBERT R., Fueyo Joanna L., KRAEMER JAMES R.
Принадлежит:

A computer implemented method, apparatus, and computer program product of determining mechanisms of action for therapies. A first set of brain scans for each subject in a plurality of subjects generated at a first time period and a second set of brain scans for each subject generated at a second time period are received. Each subject is diagnosed with a given condition and received a given therapy. A set of changes in the set of brain scans is identified for the each subject based on a comparison of a first set of regions of interest in the first set of scans for each subject with a second set of regions of interest in the second set of scans for each subject. A set of typical changes attributable to the given therapy is identified. A mechanism of action for the given therapy is generated based on the set of typical changes. 1. A computer implemented method of assessing neuroimaging and medical data to determine mechanisms of action for neuropsychiatric therapies , the computer implemented method comprising:receiving, at a processor via a network connection, neuroimaging data of a first set of human brain scans for each human subject in a plurality of human subjects diagnosed with a given neuropsychiatric condition generated at a first time period prior to beginning implementation of a neuropsychiatric therapy to treat the given neuropsychiatric condition and a second set of human brain scans for the each human subject in the plurality of human subjects diagnosed with the given neuropsychiatric condition generated at a second time period of a given amount of time after beginning the implementation of the neuropsychiatric therapy to treat the given neuropsychiatric condition, wherein a set of one or more scanning devices generate the neuroimaging data of the first set of human brain scans and the second set of human brain scans, and wherein the neuropsychiatric therapy is received by the each human subject in the plurality of human subjects diagnosed with the given ...

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Номер записи: 31
07-01-2016 дата публикации

System and method for detecting tissue and fiber tract deformation

Номер: US20160005169A1
Автор: Cameron PIRON, David GALLOP, Gal SELA, Joshua RICHMOND, Michael WOOD, Monroe M. Thomas, Murugathas YUWARAJ, Simon Alexander, Wes HODGES
Принадлежит: Synaptive Medical Barbados Inc

Disclosed herein is a method for producing an evolvable tissue model of a patient and, using this model, modelling physical transformations of the tissue (e.g. deformation) of the tissue model by interacting the tissue model with influence models which model interactions with the tissue such as surgical instruments, pressure, swelling, temperature changes etc. The model is produced from a set of input data of the tissue which includes directional information of the tissue. The directional information is used to produce an oriented tissue map. A tissue model is then produced from the oriented tissue map such that the tissue model reflects the directionality of the tissue component. When the tissue model is subjected to an influence that causes tissue deformation over a period of time, the tissue model directionally deforms over the period of time in a manner which reflects a trajectory of the influence interacting with the directionality of the tissue component.

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Номер записи: 32
13-01-2022 дата публикации

DETERMINATION OF A FURTHER PROCESSING LOCATION IN MAGNETIC RESONANCE IMAGING

Номер: US20220012876A1
Автор: HELLE MICHAEL GUNTER, SOMMER KARSTEN
Принадлежит:

The invention provides for a method of training a neural network () configured for providing a further processing location (). The method comprises providing () a labeled medical image (), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (). The method further comprises inputting () the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (). The method further comprises determine () the closest truth processing location () for the most likely trial processing location. The method further comprises calculating () an error vector () using the closest truth processing location and the most likely trial processing location. The method further comprises training () the neural network using the error vector. 1. A method of training a neural network configured for providing a further processing location , wherein the method comprises:providing a labeled medical image, wherein the labeled medical image comprises a plurality of labels each indicating a truth processing location;inputting the labeled medical image into the neural network to obtain one trial processing location, wherein the one trial processing location includes a most likely trial processing location;determine the closest truth processing location for the most likely trial processing location, wherein the closest truth processing location is the closest of the truth processing locations to the output of the neural network;calculating an error vector using the closest truth processing location and the most likely trial processing location, wherein the error vector is a position change between the closest truth processing location and the most likely trial processing location, wherein the error vector is calculated only using the closest truth processing location and the most likely trial processing location; andtraining the neural network ...

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Номер записи: 33
13-01-2022 дата публикации

LEARNING METHOD FOR GENERATING MULTIPHASE COLLATERAL IMAGE AND MULTIPHASE COLLATERAL IMAGE GENERATING METHOD USING MACHING LEARNING

Номер: US20220012883A1
Автор: KIM Hyun Jeong, KWAK Jin Tae, MINH NGUYEN Nhat To, ROH Hong Gee
Принадлежит:

A learning method for generating a multiphase collateral image comprises the steps of: receiving inputs of an MRI image of a head part and a multiphase collateral image generated on the basis of the MRI image; generating a brain mask by using the MRI image; generating an MRI image and a multiphase collateral image which are masked by the brain mask; and learning the masked multiphase collateral image for the masked MRI image by using a learning network. 1. A learning method for generating multiphase collateral images , the learning method comprising:receiving a magnetic resonance imaging (MRI) image of a head and multiphase collateral images generated on the basis of the MRI image;generating a brain mask using the MRI image;generating an MRI image and multiphase collateral images which are masked by the brain mask; andlearning the masked multiphase collateral images for the masked MRI image using a learning network.2. The learning method of claim 1 , wherein the generating of the brain mask comprises:merging a plurality of MRI images generated at a preset position in the head over time;normalizing pixel values of the merged MRI images into a preset range; andgenerating the brain mask using pixel values which are greater than or equal to a threshold value among the normalized pixel values.3. The learning method of claim 1 , wherein the learning of the masked multiphase collateral images comprises adjusting weights of a filter used for learning according to pixel-specific frequencies of the multiphase collateral images and learning the masked multiphase collateral images.4. The learning method of claim 1 , wherein the MRI image corresponds to a plurality of MRI images generated at a preset position in the head over time.5. The learning method of claim 1 , wherein the MRI image is a dynamic susceptibility contrast-magnetic resonance perfusion (DSC-MRP) image or a four-dimensional (4D)-magnetic resonance angiography (MRA) image.6. A method of generating multiphase ...

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Номер записи: 34
04-01-2018 дата публикации

DYNAMIC DEFINITION OF A REGION OF INTEREST FOR TRACKING NERVE FIBERS

Номер: US20180005380A1
Автор: Seiler Thomas, STEDELE Katrin
Принадлежит:

The invention relates to a medical data processing method for determining the position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data. 1. A method for determining a position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers , the method executing on a processor of a computer , and comprising:a) acquiring, at the processor, medical image data describing a medical image of an anatomical body part of a patient's body comprising the nerve fibers, the medical image generated by applying a diffusion-based medical imaging method to the anatomical body part;b) acquiring, at the processor, actual region size data describing the actual size of the region of interest;c) acquiring, at the processor, predetermined region size data describing a predetermined minimum size of the region of interest;d) determining, by the processor and based on the actual region size data and predetermined region size data, whether the region of interest has at least the predetermined size, and e1) acquiring, at the processor, region position data describing the position of the region of interest in the anatomical body part described by a position of a virtual tool in the medical image;', 'e2) determining, by the processor and based on the medical image data and the region position data, fiber tracking data describing a result of tracking a nerve fiber running through the region of interest;', e3a) acquiring, at the processor, changed region position data describing a new position of the region of interest different from the position described by the region position data;', 'e3b) determining, by the processor and based on the medical image data and the changed region position data, changed region tracking data describing a result of ...

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Номер записи: 35
04-01-2018 дата публикации

Image segmentation via multi-atlas fusion with context learning

Номер: US20180005381A1
Автор: Andrew Joseph ASMAN, Andrew PLASSARD, Benjamin K. POULOSE, Bennett LANDMAN, Mayur Patel, Miya SMITH, Patrick Kelly, Rebeccah B. BAUCOM, Richard G. ABRAMSOM, Zhoubing XU
Принадлежит: VANDERBILT UNIVERSITY

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

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Номер записи: 36
02-01-2020 дата публикации

3D RADIOMIC PLATFORM FOR IMAGING BIOMARKER DEVELOPMENT

Номер: US20200005461A1
Автор: Yip Stephen
Принадлежит:

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications. 1. A computer-implemented method to analyze medical image data , the method comprising:obtaining, using one or more processors, the medical image data comprising a plurality of two-dimensional (2D) medical images;performing, using the one or more processors, target tissue detection and target tissue segmentation for each 2D medical image to produce a set of segmented target tissue images;generating, using the one or more processors, a three-dimensional (3D) model of detected and segmented target tissue;identifying, using the one or more processors, a master set of radiomic features for the 3D model of detected and segmented target tissue;comparing, using the one or more processors, at least some of the radiomic features in the master set to identify redundant radiomic features for the 3D model of detected and segmented target tissue;excluding, using the one or more processors, the redundant radiomic features from the 3D model of detected and segmented target tissue; andextracting, using the one or more processors, a selected set of radiomic features from the 3D model of detected and segmented target tissue.2. The method of claim 1 , wherein the target tissue is tumor tissue.3. The method of claim 2 , wherein the tumor tissue comprises breast ...

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Номер записи: 37
03-01-2019 дата публикации

DIAGNOSTIC SUPPORT SYSTEM AND DIAGNOSTIC SUPPORT METHOD

Номер: US20190005660A1
Автор: Kawabata Shigenori, Kinoshita Akira, Niyagawa Kazuya, Takada Masahiro
Принадлежит:

A diagnostic support system includes a spinal cord/spinal nerve evoked magnetic field data acquisition device configured to acquire spinal cord/spinal nerve evoked magnetic field data and a medical image information acquisition device configured to acquire first medical image information having each pixel associated with a corresponding pixel of visualized data of the spinal cord/spinal nerve evoked magnetic field data. The diagnostic support system superimposes the visualized data of the spinal cord/spinal nerve evoked magnetic field data on second medical image information based on information included in the first medical image information. 1. A diagnostic support system comprising:a spinal cord/spinal nerve evoked magnetic field data acquisition device configured to acquire spinal cord/spinal nerve evoked magnetic field data; anda medical image information acquisition device configured to acquire first medical image information having each pixel associated with a corresponding pixel of visualized data of the spinal cord/spinal nerve evoked magnetic field data;wherein the visualized data of the spinal cord/spinal nerve evoked magnetic field data is superimposed on second medical image information based on information included in the first medical image information.2. A diagnostic support system comprising:a processor configured to execute a program stored in a memory to implement a process of generating registration data for superimposing first medical image information on second medical image information such that a position of a target organ in the first medical image information coincides with a corresponding position of the target organ in the second medical image information, the first medical image information having one or more pixels associated with corresponding pixels of reconstruction data reconstructed based on magnetic field data; anda display device configured to display superimposed image information obtained by superimposing the reconstruction ...

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Номер записи: 38
20-01-2022 дата публикации

Method and system for characterizing an impact of brain lesions on brain connectivity using mri

Номер: US20220020154A1
Автор: Jonas Richiardi, Tobias Kober, Veronica Ravano
Принадлежит: Siemens Healthcare GmbH

A system and a method for mapping lesions or damage instances of a brain. The method includes receiving a lesion segmentation mask for the brain and receiving a tractography atlas. A connectivity damage brain map is constructed from (i) superimposing the lesion segmentation mask and a tractography atlas-based image, and (ii) combining information from the lesion segmentation mask with information from the tractography atlas-based image. The tractography atlas-based image is an image obtained from the tractography atlas, and the tractography atlas-based image and the lesion segmentation mask are registered to a common space.

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Номер записи: 39
14-01-2021 дата публикации

Systems and Methods for Generating Biomarkers Based on Multivariate MRI and Multimodality Classifiers for Disorder Diagnosis

Номер: US20210007603A1
Автор: Huddleston Daniel, Mahmoudi Babak
Принадлежит:

In some embodiments, the systems and methods of the disclosure can efficiently and accurately classify neurodegenerative disorder(s) and/or movement disorder(s) of a subject (e.g., a patient) using at least quantitative features associated with one or more regions of interest determined from one or more sets of image data of the subjects brain. The method may include processing one or more sets of MRI image data of the subjects brain to extract one or more quantitative features for one or more regions. The one or more quantitative features may include a first quantitative and a second quantitative feature. The method may further include classifying at least the one or more quantitative features into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder and/or heathy control. The method may include generating a report including a classification of at least the one or more quantitative features. 1. A computer-implemented method for classifying neurodegenerative disorder(s) and/or movement disorder(s) of a subject , the method comprising:receiving subject data of a subject, the subject data including one or more sets of MRI image data of a brain of the subject;processing one or more sets of MRI image data to extract one or more quantitative features for one or more regions, the one or more quantitative features for the one or more regions includes a first quantitative feature for the one or more regions and a second quantitative feature for the one or more regions;classifying at least the one or more quantitative features for the one or more regions into one or more classes associated with neurodegenerative dementia disorder, neurodegenerative movement disorder, non-neurodegenerative movement disorder, and/or heathy control; andgenerating a report including a classification of at least the one or more quantitative features.2. The method according to claim 1 , wherein the one ...

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Номер записи: 40
11-01-2018 дата публикации

System of joint brain tumor and cortex reconstruction

Номер: US20180008187A1
Автор: Bi Song, Chen-Rui Chou, Ming-Chang Liu
Принадлежит: Sony Corp

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

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Номер записи: 41
14-01-2021 дата публикации

SYSTEMS AND METHODS FOR DETERMINING A BRAIN CONDITION OF A SUBJECT USING EARLY TIME FRAME PET IMAGE ANALYSIS

Номер: US20210007697A1
Автор: Andrews Randolph D., Lukic Ana S., Matthews Dawn C., Strother Stephen C., Wernick Miles N.
Принадлежит:

Systems and methods for use in identifying a brain condition of a subject are provided. In some aspects, a provided method includes constructing a classifier to identify a brain condition of a subject comprising steps of receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer. The method also includes defining a plurality of brain condition classes using the image data associated with one or more time frames during the acquisition period, and processing the image data to generate signatures corresponding to each of the plurality brain condition classes. The method further includes constructing the classifier using the signatures. The classifier can then be applied to determine a degree to which the subject expresses one or more disease states in order to determine a brain condition of the subject. 1receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer.. A method for constructing a classifier for identifying a brain condition of a subject, the method comprising: This application is a continuation of U.S. patent application Ser. No. 15/760,391, which represents the National Phase under 35 U.S.C. § 371 of PCT/US2016/051854, filed Sep. 15, 2016, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/219,421 filed on Sep. 16, 2015, each of which references is incorporated herein by reference in its entiretyThe present disclosure relates generally to systems and methods for determining a medical condition of a patient using imaging data and, in particular, to systems and methods for identifying the presence and/or progression of a brain condition of a subject.Neurodegenerative diseases and other syndromes affecting the brain commonly exhibit distinctive symptoms and characteristics that often ...

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Номер записи: 42
11-01-2018 дата публикации

Diagnosis support system, diagnosis support apparatus, and recording medium

Номер: US20180008223A1
Автор: Hideaki Yamagata
Принадлежит: Ricoh Co Ltd

A diagnosis support system includes a calculator configured to calculate position information indicating a positional relationship between a biological sensor and a predetermined region of a measurement target; and an extractor configured to extract, from biological information already diagnosed, biological information that is associated with position information, which is similar to the position information calculated by the calculator.

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Номер записи: 43
08-01-2015 дата публикации

Methods and systems for a high-resolution brain image pipeline and database program

Номер: US20150010223A1
Автор: Guillermo Sapiro, Noam Harel
Принадлежит: Surgical Information Sciences Inc

A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7 T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.

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Номер записи: 44
09-01-2020 дата публикации

MAGNETIC RESONANCE IMAGING APPARATUS WITH AUTO-POSITIONING FUNCTION, METHOD FOR CONTROLLING MAGNETIC RESONANCE IMAGING APPARATUS, AND PROGRAM FOR AUTO-SETTING OF IMAGING PLANE

Номер: US20200008702A1
Автор: HARADA Kuniaki, Nagao Hisako, NOGUCHI Yoshimi, Ochi Hisaaki, SAKURAGI Kenta, Yokosawa Suguru
Принадлежит: Hitachi, Ltd.

An imaging unit of an MRI apparatus performs imaging of a positioning image of a subject including a spine; a first imaging that images a cross section including the spine and extending along a longitudinal direction of the spine; and a second imaging that images a cross section in a direction of traversing the spine. An automatic cross-section position setting unit detects a specific tissue of the spine using a scout image or an image including the spine acquired in the first imaging step, performs a matching process between the detected specific tissue of the spine and a spine model, and calculates an imaging cross-section position of the second imaging based upon a specific tissue position of the spine specified by matching, thereby performing automatic setting. 1. A magnetic resonance imaging apparatus , comprising:an imaging unit configured to select a desired imaging plane of a subject to acquire a nuclear magnetic resonance signal generated from the imaging plane;a signal processing unit configured to process the nuclear magnetic resonance signal acquired from the imaging unit;a control unit configured to control the imaging unit and the signal processing unit; andan imaging cross-section position setting unit configured to automatically set a position of the imaging plane, whereinthe imaging cross-section position setting unit includes a tissue extracting unit configured to extract a specific tissue using an image acquired in advance by the imaging unit, a matching unit configured to perform a matching process on the specific tissue extracted by the tissue extracting unit using a template of the specific tissue, and a cross-section calculating unit configured to calculate a cross-section position including the specific tissue specified through the matching process.2. The magnetic resonance imaging apparatus according to claim 1 , whereinthe tissue extracting unit performs extraction of the specific tissue using a machine learning algorithm learned by ...

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Номер записи: 45
10-01-2019 дата публикации

Systems and methods for automated detection in magnetic resonance images

Номер: US20190011521A1
Автор: Gregory L. Charvat, Jonathan M. Rothberg, Michal Sofka, Tyler S. Ralston
Принадлежит: Hyperfine Research Inc

Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. The method comprises, while the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

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Номер записи: 46
14-01-2016 дата публикации

METHOD AND APPARATUS FOR DISPLAYING PATHOLOGICAL CHANGES IN AN EXAMINATION OBJECT BASED ON 3D DATA RECORDS

Номер: US20160012581A1
Автор: GRANDY Martin, HAGMANN Patric, Huwer Stefan, Krüger Gunnar, MAEDAR Philippe, MARECHAL BENEDICTE, MEULI Reto
Принадлежит:

A method and an apparatus are disclosed for analyzing pathological changes to anatomic areas in examination objects. In such cases, the method includes the segmentation of 3D data of the anatomic area, its standardization, comparison with a reference model and assignment of a deviation value and intensity value from an intensity scale to the anatomic area. The intensity value of the diagrammatic display of the standardized anatomic area allows the person skilled in the art to determine pathological changes to the anatomic area in a quick and cost-effective manner. 1. A method for analyzing a 3D data record of an examination object , comprising:segmenting the 3D data record of the examination object to determine different anatomic areas in the 3D data record;determining standardized anatomic areas by standardizing sizes of the different anatomic areas;comparing the sizes of the different standardized anatomic areas with a reference model including reference variables of the different anatomic areas; and determining a deviation value, indicating how the size of the respective standardized anatomic area deviates from the associated reference variable,', 'assigning, to all pixels of a respective anatomic area, a deviation value, so that all pixels of an anatomic area have the same deviation value, and', 'assigning, to all pixels of the respective anatomic area, an intensity value for diagrammatic display which corresponds to the deviation value., 'for each of the standardized anatomic areas,'}2. The method of claim 1 , wherein claim 1 ,all of the different anatomic areas, contained in the 3D data record of the examination object, are divided into a plurality of pixels, andthe standardization of a respective one of the different anatomic areas takes place such that the ratio of the volume of all pixels of the respective one of the different anatomic areas with an overall volume of all pixels of the examination object is determined.3. The method of claim 1 , wherein claim ...

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Номер записи: 47
03-02-2022 дата публикации

SYSTEMS AND METHODS FOR TRACKING A SURGICAL DEVICE

Номер: US20220031400A1
Автор: Bustan Itamar, Hod Uriel, Levi Moran, Rachli Noam, Wolfson Helen
Принадлежит: Biosense Webster (Israel) Ltd.

Systems, methods, and devices for registering a 3D image of a patient with magnetic coordinates are disclosed. A tri-axial sensor (TAS) may be added to the 3D camera. The location and orientation of the TAS sensor may be determined based on the known magnetic fields that are applied by a magnetic field transmitter. The camera coordinate system may then be transferred to the magnetic coordinate system. After completing the registration of the 3D image with a CT image and the 3D image with the magnetic coordinates, the CT image may then be registered with the magnetic coordinates. 1. A method comprising:activating an electromagnetic tracking system on a head of a patient;receiving a three-dimensional (3D) image of the head of the patient and storing the 3D image a scatter plot in a memory, wherein the 3D image is acquired using a 3D camera comprising at least one optical sensor and at least one magnetic sensor;determining a location and an orientation of the 3D camera when the 3D image was acquired via magnetic sensor based on known magnetic fields applied by one or more transmitters of the electromagnetic tracking system;registering the scatter plot to magnetic coordinates of the electromagnetic tracking system using the determined location and orientation of the 3D camera, wherein the registered scatter plot is stored in the memory.2. The method of claim 1 , wherein the at least one magnetic sensor is a tri-axial sensor (TAS).3. The method of claim 1 , wherein the at least one magnetic sensor is a single-axis sensor (SAS) or dual-axis sensor (DAS).4. The method of claim 1 , wherein the scatter plot is a reference fixed to two separate optical sensors on the 3D camera.5. The method of claim 1 , further comprising registering the received 3D image of the subject with a computerized tomography (CT) image of the subject.6. The method of claim 5 , wherein the CT image is registered with the magnetic coordinates using an Iterative Closest Point (ICP) algorithm.7. The ...

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Номер записи: 48
11-01-2018 дата публикации

Methods For Improved Measurements Of Brain Volume and Changes In Brain Volume

Номер: US20180012354A1
Автор: Fisher Elizabeth
Принадлежит: Biogen MA Inc.

Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively. 1. A method of calculating brain volume of a patient , the method comprising:obtaining a first set of medical images of the patient's brain at a first time point, wherein the first set of medical images includes at least two medical images;obtaining a second set of medical images of the patient's brain at a second time point, wherein the second set of medical images includes at least two medical images;calculating, with a first algorithm, a first brain volume value at the first time point based on two or more images from the first set of medical images and a second brain volume value at the second time point based on two or more images from the second set of medical images;calculating, with a second algorithm, a third brain volume value at the first time point based on two or more images from the first set of medical images and a fourth brain volume value at the second time point based on two or more images from the second set of medical images;applying a mathematical weight to at least one of the first brain volume value, the second brain volume value, the third brain volume value, or the fourth brain volume value;averaging the first brain volume value and the third ...

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Номер записи: 49
11-01-2018 дата публикации

Adaptation of Image Data Sets to an Updated Atlas-Based Reference System

Номер: US20180012358A1
Автор: Varkuti Bálint
Принадлежит:

The invention relates to a computer-implemented medical data processing method for determining a mapping of medical image content into a reference system, the method comprising executing, on a processor of a computer, steps of: a) acquiring, at the processor, medical image data describing a digital medical image of an anatomical structure of a patient's body; b) acquiring, at the processor, image attribute data describing attribute information associated with the medical image data, the attribute information including an indication of an initial reference system in which spatial relationships of the digital medical image are defined; c) acquiring, at the processor, reference system transformation data describing a spatial relationship (REG) between the initial reference system and a second reference system which is different from the initial reference system; d) determining, by the processor and based on the medical image data and the reference system transformation data, transformed image data describing a representation of the digital medical image in the second reference system. 115-. (canceled)16. A computer-implemented method for determining a mapping of medical image content into a reference system , the method comprising executing , on at least one processor , steps ofacquiring, by the at least one processor, medical image data describing a digital medical image of an anatomical structure of a patient's body;acquiring, by the at least one processor, image attribute data describing attribute information associated with the medical image data, the attribute information including an indication of an initial reference system in which positions in the digital medical image are defined wherein the initial reference system is defined by the spatial relationships in a first atlas;acquiring, by the at least one processor, reference system transformation data describing a spatial relationship between the initial reference system and a second reference system which is ...

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Номер записи: 50
10-01-2019 дата публикации

SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR THE DETERMINATION OF ACCELERATED BRAIN ATROPHY AND AN OPTIMAL DRAINAGE SITE FOR A SUBDURAL HEMATOMA USING COMPUTED TOMOGRAPHY

Номер: US20190012783A1
Автор: MIKHEEV ARTEM, Rusinek Henry, SAMADANI UZMA, ZAHID ABDULLAH BIN
Принадлежит:

To that end, in order to overcome some of the deficiencies presented herein above, an exemplary system, method and computer-accessible medium for determining an attribute(s) of a brain of a patient, can include, for example, receiving information obtained from a computed tomography (“CT”) scan(s) of a portion(s) of the brain, generating a CT image(s) that can be based on the information, and determining the attribute(s) of the brain based on the CT image(s) by segmenting an intracranial space (ICS) in the CT image(s). The attribute(s) can include a presence or absence of Alzheimer's disease, total volume of the ICS, brain, CSF or a lesion or the volumes of ICS, brain, CSF or lesion(s) expressed as a percentage of other volume(s). The aforementioned areas can be segmented using a combination of thresholding, morphological erosions, morphological dilations, manual segmentation or semi-automatic segmentation techniques, all of which can be parallel procedures. These attributes can be further used to determine treatment, for example, optimizing the location of the twist drill craniotomy to drain hematoma in subdural hematoma. 1. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for determining at least one attribute of a brain of a patient , wherein , when a computer arrangement executes the instructions , the computer arrangement is configured to perform procedures comprising:receiving information obtained from at least one computed tomography (CT) scan of at least one portion of the intracranial cavity;generating at least one CT image based on the information; anddetermining the at least one attribute of the area(s) of interest identified based on the at least one CT image by segmenting an intracranial space (ICS) in the at least one CT image.2. The computer-accessible medium of claim 1 , wherein the at least one attribute includes at least one of (i) a brain volume claim 1 , (ii) a cerebral spinal fluid volume claim 1 ...

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Номер записи: 51
19-01-2017 дата публикации

Task-less optical mapping of brain function using resting state functional connectivity

Номер: US20170014066A1
Автор: Abraham Z. Snyder, Bradley L. Schlaggar, Brian R. White, Joseph P. Culver, Justin L. Vincent, Marcus E. Raichle, Michael D. Fox
Принадлежит: Washington University in St Louis WUSTL

A method for utilizing an optical system for taskless mapping of brain function includes determining a time series of dynamic light measurements for a plurality of spatially distributed source-detector pairs, receiving the dynamic light measurements over a period of time using the source-detector pairs without dependence on either a task or a change in physiological condition, generating a plurality of temporal correlations between regions of a brain for the light measurements based on the time series of the spatially distributed source-detector pairs and the received dynamic light measurements, producing at least one map of a respective strength of each of a plurality of temporal correlations, producing overlapping source-detector pairs measurements using diffuse optical tomography (DOT) geometries, reconstructing data representative of the dynamic light measurements into an image space using at least one DOT algorithm, and co-registering DOT voxel images obtained by the reconstruction to anatomical information.

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Номер записи: 52
21-01-2016 дата публикации

SYSTEMS AND METHODS FOR EMULATING DEXA SCORES BASED ON CT IMAGES

Номер: US20160015347A1
Автор: Bregman-Amitai Orna, ELNEKAVE Eldad
Принадлежит:

There is provided a computerized method for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion; segmenting the bone portion from the imaging data; computing at least one grade based on pixel associated values from the bone portion; and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan; wherein the grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

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Номер записи: 53
21-01-2016 дата публикации

CONTEXT AWARE SURGICAL SYSTEMS

Номер: US20160015471A1
Автор: ALEXANDER Simon, Gallop David, Hodges Wes, Lau William, McFadyen Stephen, Panther Alex, Piron Cameron, Richmond Joshua, Sela Gal, Shanmugaratnam Nishanthan, Thomas Monroe M., Wood Michael, Yuwaraj Murugathas
Принадлежит: SYNAPTIVE MEDICAL (BARBADOS) INC.

Systems and methods are provided in which devices that are employed during a medical procedure are adaptively configured during the medical procedure, based on input or feedback that is associated with the current state, phase or context of the medical procedure. In some example embodiments, the input is obtained via the identification of one or more medical instruments present within a region of interest, and this input may be employed to determine configuration parameters for configuring the device. In other example embodiments, the input may be based on the image-based detection of a measure associated with the phase or context of the medical procedure, and this input may be employed to adaptively control the device based on the inferred context or phase of the medical procedure. In other embodiments, images from one imaging modality may be employed to adaptively switch to another imaging modality. 1. A computer implemented method of adaptively and intraoperatively configuring a device used during a medical procedure , the method comprising:identifying a medical instrument during the medical procedure;obtaining one or more customized configuration parameters for adaptively configuring the device during the medical procedure, where the customized configuration parameters are selected based on the identity of the medical instrument; andconfiguring the device according to the customized configuration parameters.2. The method according to wherein the medical instrument is automatically identified.3. The method according to wherein the medical instrument is identified by:detecting a signal from one or more fiducial markers associated with the medical instrument; andprocessing the signal to identify the medical instrument.4. The method according to wherein the one or more fiducial markers are selected from the group consisting of passive markers claim 3 , active markers claim 3 , glyphs and RFID tags.5. The method according to wherein the medical instrument is ...

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Номер записи: 54
17-01-2019 дата публикации

PERFUSION DIGITAL SUBTRACTION ANGIOGRAPHY

Номер: US20190015061A1
Автор: Liebeskind David S., Scalzo Fabien
Принадлежит: The Regents of the University of California

An apparatus and methodological framework are provided, named perfusion angiography, for the quantitative analysis and visualization of blood flow parameters from DSA images. The parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and T, are computed using a bolus tracking method based on the deconvolution of time-density curves on a pixel-by-pixel basis. Individual contrast concentration curves of overlapping vessels can be delineated with multivariate Gamma fitting. The extracted parameters are each transformed into parametric maps of the target that can be color coded with different colors to represent parameter values within a particular set range. Side by side parametric maps with corresponding DSA images allow expert evaluation and condition diagnosis. 1. An apparatus for quantitative analysis and visualization of blood flow parameters from digital subtraction angiography (DSA) images , the apparatus comprising:(a) an x-ray imager;(b) a computer processor; and (i) acquire DSA image data of a subject from the imager;', '(ii) calculate concentration time curves of the arterial input function from the DSA images;', '(iii) extract perfusion parameters from the DSA images and concentration time curves;', '(iv) compute parametric maps of each extracted perfusion parameter data and DSA image data; and', '(v) display the parametric maps and DSA images on a visual display., '(c) programming residing in a non-transitory computer readable medium, wherein the programming is executable by the computer processor and configured to2. The apparatus of claim 1 , further comprising color coding the parametric maps with a color indicating a perfusion parameter value is above or below a threshold value.3. The apparatus of claim 1 , wherein said concentration time curves of the arterial input function are calculated by averaging DSA concentration values within a region of interest at each time point.4. The ...

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Номер записи: 55
21-01-2021 дата публикации

SYSTEM FOR ANALYSING AN ACTIVITY OF BRAIN USING MAGNETIC RESONANCE IMAGING (MRI) DATA

Номер: US20210015366A1
Автор: Agrawal Rimjhim
Принадлежит:

A system for classifying an activity and connectivity of a brain into at least one neuropsychiatric disorder from magnetic resonance imaging (MRI) images. The system includes an imaging device, a network, and a brain activity analyzing server. The system (i) generate a three-dimensional (3D) structural MRI image and a 4D functional MRI images of the brain, (ii) extracts one or more features associated with one or more regions of the brain using a parcellation scheme, (iii) analyses, using a machine learning model, an intensity of at least one voxel in the one or more regions, and (iv) classifies the activity and the connectivity of the brain into at least one neuro-psychiatric disorder based on a percentage of variation of intensity of the at least one voxel in the one or more regions of the brain over the one or more features from a predefined threshold value. 1. A processor-implemented method for classifying an activity and connectivity of a brain into at least one neuropsychiatric disorder from magnetic resonance imaging (MRI) images , wherein the method comprising:obtaining a plurality of two-dimensional slices of structural MRI images and a plurality of two-dimensional slices of functional MRI images of the brain;generating a three-dimensional structural MRI image and a four-dimensional functional MRI image of the brain from the plurality of two-dimensional slices of structural MRI images and the plurality of two-dimensional slices of functional MRI images of the brain;extracting a plurality of features associated with a plurality of regions of the brain in a co-registered MRI image of the brain, wherein the co-registered MRI image is created by co-registering the three-dimensional structural MRI image to the four-dimensional functional MRI image, wherein the co-registered MRI image is parcellated into the plurality of regions of the brain using a parcellation scheme;analysing, using a machine learning model, an intensity of at least one voxel in the plurality ...

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Номер записи: 56
19-01-2017 дата публикации

A MEDICAL SCANNING SYSTEM AND METHOD FOR DETERMINING SCANNING PARAMETERS BASED ON A SCOUT IMAGE

Номер: US20170018078A1
Автор: Dong Jiaqin, Liu Ping, Wu Jie
Принадлежит:

A medical scanning system and method for determining scanning parameters based on a scout image, the system includes: a scanned object description module for describing the shape of a scanned object on an initial image; an adjustment module for aligning the shape of the scanned object with the pre-stored average shape; a principal component analysis module for extracting the principal component for the aligned shape of the scanned object; a desired shape acquisition module for imparting weight parameters to said principal component, acquiring a plurality of new shapes, and from said plurality of new shapes, determining the new shape with the maximum cost function value as the desired shape and a scanning parameter setting module for setting scanning parameters based on the desired shape. 1. A medical scanning system for determining scanning parameters , comprising:a scanned object description module for arranging a plurality of control points for describing the shape of a scanned object on an initial scout image;an adjustment module for aligning the shape of the scanned object with a previously stored average shape by linearly transforming the control points for describing the shape of the scanned object;a principal component analysis module for exacting the principal component of the aligned shape of the scanned object by means of a principal component analysis algorithm; [{'br': None, 'i': [{'o': {'@ostyle': 'single', 's'}, 's′=+P'}, 'b, 'sub': s', 'new}, {'o': {'@ostyle': 'single', 's'}, 'sub': 'new', 'where, s′ is used to describe the new shape, is used to describe the average shape, Ps represents the principal component, brepresents the weight parameter of the principal component;'}], 'a desired shape acquisition module for imparting a plurality of weight parameters to the principal component, acquiring a plurality of new shapes by means of the following equation, and determining, from the plurality of new shapes, a new shape with the maximum cost function ...

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Номер записи: 57
03-02-2022 дата публикации

CRANIAL CT-BASED GRADING METHOD AND SYSTEM

Номер: US20220036553A1
Автор: JIN Hailan, LI Pengxiang, Qin Lan, SONG Ling, Yang Guangming, YAO Yangyang, Yin Yin
Принадлежит:

Disclosed are a cranial CT-based grading method and a corresponding system, which relate to the field of medical imaging. The cranial CT-based grading method as disclosed solves the problems of relatively great subjective disparities and poor operability in eye-balling ASPECTS assessment. The grading method includes: determining frames where target image slices are located from to-be-processed multi-frame cranial CT data; extracting target areas; performing infarct judgment on each target area included in the target areas to output an infarct judgment outcome regarding the target area; and outputting a grading outcome based on infarct judgment outcomes regarding all target areas. The grading method and system as disclosed may eliminate or mitigate the diagnosis disparities caused by human factors and imaging deviations due to different imaging devices, and shorten the time taken by human observation, consideration, and bared-eye grading, thereby serving as a computer-aided method to provide reference for medical studies on stoke. 1. A cranial CT-based grading method , comprising:determining frames where target image slices are located from to-be-processed multi-frame cranial CT data, wherein the target image slices refer to the slices, where the cranial CT data for being graded are located, in the cranial CT;extracting target areas based on the frames where the target image slices are located, wherein the target areas refer to areas for being graded in the cranial CT data;performing infarct judgment on each target area included in the target areas to output an infarct judgment outcome regarding the target area; andoutputting a grading outcome based on infarct judgment outcomes regarding all target areas.2. The method according to claim 1 , wherein the determining frames where target image slices are located from to-be-processed multi-frame cranial CT data specifically comprises:extracting image features of the to-be-processed multi-frame cranial CT data, and ...

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Номер записи: 58
21-01-2016 дата публикации

VASCULAR TERRITORY SEGMENTATION USING MUTUAL CLUSTERING INFORMATION FROM IMAGE SPACE AND LABEL SPACE

Номер: US20160019692A1
Автор: Jung Youngkyoo
Принадлежит:

Methods, systems, computer programs, circuits and workstations are configured to generate at least one two-dimensional weighted CBF territory map of color-coded source artery locations using an automated vascular segmentation process to identify source locations using mutual connectivity in both image and label space.

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Номер записи: 59
21-01-2016 дата публикации

Systems and methods for generating biomarkers based on multivariate classification of functional imaging and associated data

Номер: US20160019693A1
Автор: David Silbersweig, Emily Stern, Hong Pan
Принадлежит: Brigham and Womens Hospital Inc

Systems and methods for generating biomarkers associated with neuropsychiatric disorders, neurodevelopmental disorders, neurobehavioral disorders, or other neurological disorders are described. In general, the biomarkers are generated based on correlations between functional imaging data and clinical acquired from a subject, as computed using a multivariate classifier. Functional imaging data may include functional magnetic resonance images, or activation maps generated from such images. Clinical data generally includes data associated with a clinical or behavioral characterization of the subject. The biomarkers can be used to monitor or otherwise assess a treatment response; to provide diagnostic information, such as subtyping or classifying a disorder; to provide prognostic information, such as a prediction of treatment response or outcome; or to indicate functional or anatomical targets for treatments.

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Номер записи: 60
21-01-2016 дата публикации

Edge detection in images

Номер: US20160019699A1
Автор: Miguel C. Mudge
Принадлежит: Welch Allyn Inc

An edge detection engine operates to scan an image to identify edges within the image. An annular aperture is used to locate the edges in the image. An output image is generated by the edge detection engine that identifies the locations of the edges found in the image.

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Номер записи: 61
18-01-2018 дата публикации

AUTO-CALIBRATION OF PROBABILISTIC TRACKING PARAMETERS FOR DTI FIBRE TRACTOGRAPHY AND COMPILATION OF TRACT PROBABILITY COMPARISON SCALES

Номер: US20180018790A1
Автор: Lachner Rainer, Varkuti Bálint
Принадлежит:

. A medical data processing method of determining information describing the probable position of a neural fibre in a patient's brain, the method comprising the following steps which are constituted to be executed by a computer: a) acquiring patient-specific medical image data describing the brain of the patient; b) acquiring atlas data defining an image-based model of a human brain; c) determining, based on the patient-specific medical image data and the atlas data, seed region data describing seed regions (A, B) in the patient-specific medical image data in which the ends of neural fibres of the patient's brain may be located; d) determining, based on the patient-specific medical image data and the seed region data, neural fibre tract data describing a plurality of potential tracts (T1, T2, T3) which a specific neural fibre may take through the patient's brain; e) determining, based on the atlas data and the neural fibre tract data, a figure of merit for each one of the potential tracts (T1, T2, T3). 115.-. (canceled)16. A medical system for determining information describing a probable position of a neural fibre in at least part of a patient's brain , the system comprising: acquire, at the at least one processor, patient-specific medical image data describing the at least part of the brain of the patient;', 'acquire, at the at least one processor, atlas data defining an image-based model of at least part of a human brain;', "determine, by the at least one processor and based on the patient-specific medical image data and the atlas data, seed region data describing seed regions in the patient-specific medical image data in which the ends of neural fibres of the patient's brain may be located,", 'wherein the seed region data is determined by determining a transformation between the atlas data and the patient-specific medical image data, and determining the position of white brain matter in the patient-specific medical image data based on the transformation, wherein ...

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Номер записи: 62
17-01-2019 дата публикации

MEDICAL IMAGE PROCESSING APPARATUS, METHOD, AND PROGRAM

Номер: US20190019304A1
Автор: NAKAMURA Keigo, TAKEI Mizuki
Принадлежит: FUJIFILM Corporation

An image acquisition unit acquires a brain image of a subject. A non-bleeding region specifying unit specifies a non-bleeding region in the brain image, and a selection unit selects a standard brain image corresponding to at least one of the shape or the size of the non-bleeding region from a plurality of standard brain images. Then, a division unit divides the brain included in the brain image into regions based on the selected standard brain image. 1. A medical image processing apparatus , comprising:an image acquisition unit that acquires a brain image including a brain of a subject;a storage unit that stores a plurality of standard brain images having a plurality of types of shapes and sizes;a non-bleeding region specifying unit that specifies a non-bleeding region in the brain image;a selection unit that selects a standard brain image corresponding to at least one of a shape or a size of the non-bleeding region from the plurality of standard brain images; anda division unit that divides a brain included in the brain image into regions based on the selected standard brain image.2. The medical image processing apparatus according to claim 1 ,wherein the non-bleeding region is one or more of at least one of a plurality of sulci, at least one of a plurality of cerebral ventricles, and at least one of a plurality of anatomical regions in a subarachnoid space.3. The medical image processing apparatus according to claim 1 ,wherein the non-bleeding region specifying unit specifies the non-bleeding region based on diagnostic information of the subject.4. The medical image processing apparatus according to claim 1 ,wherein the non-bleeding region specifying unit specifies the non-bleeding region based on the brain image.5. The medical image processing apparatus according to claim 1 , further comprising:a diseased region specifying unit that specifies a diseased region including a disease in the region-divided brain image.6. The medical image processing apparatus ...

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Номер записи: 63
16-01-2020 дата публикации

VALIDITY OF A REFERENCE SYSTEM

Номер: US20200020101A1
Автор: Mewes Philip, MUELLER Gunter
Принадлежит: Siemens Healthcare GmbH

In an embodiment, a method includes acquiring a first image data set of the patient, via an X-ray apparatus, at a first time point during the operative intervention, the first image data set including the reference structure, the anatomical structure and the reference system between the reference structure and the anatomical structure; acquiring a second image data set of the patient at a second time point, the second image data set including at least the reference structure; registering the second image data set to the first image data set. As a result of the registering of the second image data set to the first image data set, a registered second image data set is determined. Finally, an embodiment of the method includes determining the validity of the reference system by a comparison of the registered second image data set with the first image data set. 1. A method for a determination of a validity of a reference system between a reference structure and an anatomical structure during an operative intervention on a patient , the method comprising:acquiring a first image data set of the patient, via an X-ray apparatus, at a first time point during the operative intervention, the first image data set including the reference structure, the anatomical structure and the reference system between the reference structure and the anatomical structure;acquiring a second image data set of the patient at a second time point, via the X-ray apparatus, during the operative intervention, wherein the second image data set includes at least the reference structure;registering the second image data set to the first image data set, wherein the reference structure represented in the first image data set and the reference structure represented in the second image data set are entered as input parameters in the registering and wherein, as a result of the registering of the second image data set to the first image data set, a registered second image data set is determined, anddetermining ...

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Номер записи: 64
21-01-2021 дата публикации

Systems and methods for analytical detection of aneurysms

Номер: US20210019879A1
Автор: Kristen Catherine Karman-Shoemake, Mohamed Haithem Babiker, Val Smiricinschi
Принадлежит: Ischemaview Inc

Systems and methods for detecting an aneurysm are disclosed. The method includes forming a virtual skeleton model. The virtual skeleton model has a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of the plurality of blood vessel surface points. The method includes virtually fitting elliptically shaped tubules for each edge of the virtual skeleton model and identifying a potential aneurysm based on the fitted elliptically shaped tubules.

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Номер записи: 65
21-01-2021 дата публикации

METHOD AND DEVICE FOR COMPUTED TOMOGRAPHY IMAGING

Номер: US20210019882A1
Автор: Hofmann Bernd, KUEMMEL Florian, MUELLER Kerstin
Принадлежит: Siemens Healthcare GmbH

A method is for computed tomography imaging. In an embodiment, the method includes provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method; suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; and analyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type. A corresponding device, a control device for a computed tomography system or a diagnosis system, and a diagnosis system and a computed tomography system are also disclosed. 1. A method for computed tomography imaging , comprising:provisioning a CT data set of an object, the CT data set being previously recorded via a multispectral recording method;suppressing a contrast, caused by a tissue type, and generating a contrast-suppressed data set from the CT data set provisioned; andanalyzing at least the contrast-suppressed data set generated or a data set generated via a machine learning algorithm based on the contrast-suppressed data set, the analyzing being configured to identify at least one change in the tissue type.2. The method of claim 1 , wherein the provisioning of the CT data set of the object comprises:recording within framework of the multispectral recording method, wherein in recording of the CT data set provisioned, a multispectral resolving detector is used.3. The method of claim 1 , wherein claim 1 , during the analyzing claim 1 , at least one of a position of an area having a change within at least one of the tissue type and a size of the tissue type claim 1 , a characteristic of the change and a progress of the change is determined claim 1 , wherein the analyzing is performed via a machine learning algorithm based on models from at least one of:Markov models, neural networks, ...

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Номер записи: 66
22-01-2015 дата публикации

METHOD AND APPARATUS FOR SELECTING SEED AREA FOR TRACKING NERVE FIBERS IN BRAIN

Номер: US20150023556A1
Автор: CHO Hyug-rae, KIM Hei-soog, LEE Jong-Min, PARK Jun-sung
Принадлежит:

A method for selecting a seed area for tracking nerve fibers in a brain includes performing registration of an atlas which shows a plurality of areas which are included in the brain and image data which relates to the brain, displaying a brain area list with respect to the plurality of areas, selecting a first area from the atlas based on a first user input with respect to the brain area list, extracting an area of the image data which corresponds to the first area, as a seed area, based on a result of the registration, and generating a first image which corresponds to the seed area from the image data, and displaying the generated first image. 1. A method for selecting a seed area for tracking nerve fibers in a brain , the method comprising:performing a registration of an atlas which shows a plurality of areas which are included in the brain and image data which relates to the brain;displaying a brain area list with respect to the plurality of areas;selecting a first area from the atlas based on a first user input with respect to the brain area list;extracting, as the seed area, an area of the image data which corresponds to the first area, based on a result of the performing the registration; andgenerating a first image which corresponds to the seed area from the image data, and displaying the generated first image.2. The method of claim 1 , wherein a plurality of nerve fibers which pass through the seed area are displayed in the first image.3. The method of claim 1 , wherein the atlas includes a white matter atlas.4. The method of claim 1 , wherein the image data includes magnetic resonance imaging (MRI) data which is obtainable by using a diffusion tensor imaging (DTI) technique.5. The method of claim 1 , wherein the performing the registration of the atlas comprises:obtaining a fractional anisotropy (FA) map from the image data; andperforming a registration of the atlas and the obtained FA map.6. The method of claim 1 , wherein the generating and displaying the ...

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Номер записи: 67
24-01-2019 дата публикации

METHOD AND SYSTEM FOR GENERATING A CONTRAST ENHANCEMENT MAP

Номер: US20190021624A1
Автор: Warntjes Marcel
Принадлежит: Synthetic MR AB

The present document relates to a method for generating a contrast enhancement map of a portion of a patient. The method comprises acquiring a magnetic resonance, MR, quantification sequence of the portion, wherein the MR quantification sequence comprises quantification information of a longitudinal R1 relaxation rate, R1, and proton density, PD, of the portion, generating an R1 map of the portion based on the MR quantification sequence, wherein a value of R1 for each voxel of the R1 map is determined, generating a PD map of the portion based on the MR quantification sequence, wherein a value of PD for each voxel of the PD map is determined, estimating a R1′ map of the portion, based on the PD map and a predetermined relationship of R1 and PD of the portion, wherein an estimated value of R1 for each voxel of the R1′ map is calculated, generating a delta R1 map based on the R1 map and the R1′ map. The voxels of the R1 map, of the PD map, of the R1′ map, and of the delta R1 map, have a one to one correspondence. A value of each voxel of the delta R1 map represents a difference of R1 values of the corresponding voxel of the R1 map and of the corresponding voxel of the R1′ map. 1. A method for generating a contrast enhancement map of a portion of a patient , comprising:acquiring, by an MR scanning device, a magnetic resonance, MR, quantification sequence of the portion, wherein the MR quantification sequence comprises quantification information of a longitudinal R1 relaxation rate, R1, and proton density, PD, of the portion,generating, by a processing circuit, an R1 map of the portion based on the MR quantification sequence, wherein a value of R1 for each voxel of the R1 map is determined,generating, by the processing circuit, a PD map of the portion based on the MR quantification sequence, wherein a value of PD for each voxel of the PD map is determined,estimating, by the processing circuit, a R1′ map of the portion, based on the PD map and a predetermined relationship ...

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Номер записи: 68
24-01-2019 дата публикации

Microwave Tomography System

Номер: US20190021626A1
Автор: Anastasios GARETSOS, Constantinos KAKOYIANNIS, Efthymios Kallos, George Palikaras, Georgios STRATAKOS, Helena CANO GARCIA, Ioannis GOUZOUASIS, Ioannis SOTIRIOU, Irene KARANASIOU, Maria KOUTSOUPIDOU, Michail GARGALAKOS, Nadine GEDDES, Nikolaos Uzunoglu, Panagiotis KOSMAS
Принадлежит: Medical Wireless Sensing Ltd

A novel medical imaging system that is based on radio-wave signals at microwave frequencies and has unique properties. The system can be used for various diagnostic applications such as breast cancer detection, brain stroke detection, and assessment of internal bleeding (trauma emergencies).

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Номер записи: 69
24-01-2019 дата публикации

METHODS AND SYSTEMS FOR CLASSIFICATION AND ASSESSMENT USING MACHINE LEARNING

Номер: US20190021677A1
Автор: Eibenberger Eva, Grbic Sasa, Grosskopf Stefan, HOELZER Philipp, Shah Amitkumar Bhupendrakumar, Soza Grzegorz, SUEHLING Michael
Принадлежит: Siemens Healthcare GmbH

In one example embodiment, a method for assessing a patient include determining scan parameters of the patient using deep learning, scanning the patient using the determining scan parameters to generate at least one three-dimensional (3D) image, detecting an injury from the 3D image using the deep learning, classifying the detected injury using the deep learning and assessing a criticality of the detected injury based on the classifying using the deep learning. 1. A method for assessing a patient , the method comprising:determining scan parameters of the patient using machine learning;scanning the patient using the determined scan parameters to generate at least one three-dimensional (3D) image;detecting an injury from the 3D image using the machine learning;classifying the detected injury using the machine learning; andassessing a criticality of the detected injury based on the classifying using the machine learning.2. The method of claim 1 , further comprising:quantifying the classified injury, the assessing assesses the criticality based on the quantifying.3. The method of claim 2 , wherein the quantifying includes claim 2 ,determining a volume of the detected injury using the machine learning.4. The method of claim 2 , wherein the quantifying includes claim 2 ,estimating a total blood loss using the machine learning.5. The method of claim 1 , further comprising:selecting one of a plurality of therapeutic options based on the assessed criticality using the machine learning.6. The method of claim 1 , further comprising:displaying the detected injury in the image; anddisplaying the assessed criticality over the image.7. The method of claim 6 , wherein the displaying the assessed criticality includes providing an outline around the detected injury claim 6 , a weight of the outline representing the assessed criticality.8. A system comprising:a memory storing computer-readable instructions; and determine scan parameters of a patient using machine learning,', 'obtain a ...

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Номер записи: 70
22-01-2015 дата публикации

Medical treatment apparatus, control device and control method

Номер: US20150025295A1
Автор: Kyoka Sugiura, Takeshi Mita, Yasunori Taguchi
Принадлежит: Toshiba Corp

According to an embodiment, a medical treatment apparatus includes: a first acquirer to acquire a group including five or more pairs of corresponding points on a first perspective image of a subject captured at a first timing and a second perspective image of the subject captured at a second timing; a second acquirer to acquire a first parameter including position/orientation information of an imaging device capturing the first perspective image and conversion information related to a coordinate system of the first perspective image, and acquire a second parameter including position/orientation information of an imaging device capturing the second perspective image and conversion information related to a coordinate system of the second perspective image; a calculator to calculate difference in position of the subject between the first timing and the second timing using the group and the parameters; and a controller to control a subject position using the difference.

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Номер записи: 71
22-01-2015 дата публикации

Method for displaying image data of body regions of a patient, the body regions being prone to dementia, and a medical imaging system which is designed to carry out the method

Номер: US20150025371A1
Автор: Bjorn Heismann, Jens-Christoph Georgi, Martin Tasler, Sebastian Schmidt
Принадлежит: SIEMENS AG

A method is disclosed for displaying image data of body regions of a patient using a medical imaging system including a first imaging apparatus and a positron emission tomography apparatus, the body regions being prone to dementia. The method includes provision of first image data recorded using the first imaging apparatus; provision of second image data recorded using the positron emission tomography apparatus, the first image data and the second image data being recorded simultaneously or at short intervals of time consecutively; segmentation of the first image data in respect of body regions prone to dementia, a segmentation mask being generated to this end on the basis of the first image data; generation of results data, the results data including a selection of voxels in the second image data made using the segmentation mask; and display of the results data.

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Номер записи: 72
28-01-2016 дата публикации

MEDICAL IMAGE PROCESSING APPARATUS AND METHOD

Номер: US20160026761A1
Автор: GULAKA Praveen, JO Hyun-hee, JO Jae-moon, YOO Se-Jin
Принадлежит: SAMSUNG ELECTRONICS CO., LTD.

A medical image processing apparatus and method are provided. The medical image processing apparatus includes a controller configured to acquire an icon based on information in a medical image file of an object, the icon having a shape of the object and comprising one or more sub-icons, and a display configured to display the icon. Each of the one or more sub-icons corresponds to an anatomical region of the object is configured to accept input to perform one or more image processing functions associated with the corresponding anatomical region. 1. A medical image processing apparatus comprising:a controller configured to acquire an icon based on information in a medical image file of an object, the icon having a shape of the object and comprising one or more sub-icons; anda display configured to display the icon,wherein each of the one or more sub-icons corresponds to an anatomical region of the object and is configured to accept input to perform one or more image processing functions associated with a corresponding anatomical region.2. The medical image processing apparatus of claim 1 , wherein in response to one sub-icon from among the one or more sub-icons being selected by a user claim 1 , the controller controls the display to display a shortcut menu that provides the one or more image processing functions corresponding to the selected sub-icon.3. The medical image processing apparatus of claim 2 , wherein in response to one image processing function from among the one or more image processing functions displayed in the shortcut menu being selected by the user claim 2 , the controller performs image processing by using the selected image processing function on the medical image file.4. The medical image processing apparatus of claim 3 , wherein the one or more image processing functions displayed in the shortcut menu are selected by the user or selected according to a frequency of use.5. The medical image processing apparatus of claim 1 , a plurality of ...

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Номер записи: 73
26-01-2017 дата публикации

MEDICAL IMAGE PROCESSING APPARATUS

Номер: US20170024888A1
Автор: Ishii Hideaki, Ohyu Shigeharu, SAKASHITA Naotaka
Принадлежит: Toshiba Medical Systems Corporation

A medical image processing apparatus according to an embodiment includes processing circuitry and a display. The processing circuitry obtains first imaged data taken of a subject and second imaged data taken of the subject on a date/time different from the date/time on which the first imaged data was taken. The processing circuitry generates estimation data by performing an image processing process that changes the first imaged data on the basis of a predetermined change model. The processing circuitry generates a display image indicating the difference between the estimation data and the second imaged data. The display displays the display image. 1. A medical image processing apparatus comprising: obtain first imaged data taken of a subject and second imaged data taken of the subject on a date/time different from a date/time on which the first imaged data was taken,', 'generate estimation data by performing an image processing process that changes the first imaged data on a basis of a predetermined change model, and', 'generate a display image indicating a difference between the estimation data and the second imaged data; and, 'processing circuitry configured to'}a display configured to display the display image.2. The medical image processing apparatus according to claim 1 , further comprising: input circuitry configured to receive an input operation from an operator claim 1 , whereinthe processing circuitry is configured to generate the estimation data while changing a change amount in the image processing process in accordance with the input operation.3. The medical image processing apparatus according to claim 2 , wherein the processing circuitry is configured to perform the image processing process on the first imaged data after changing a parameter used in the image processing process on a basis of the dates/times on which the first imaged data and the second imaged data were taken.4. The medical image processing apparatus according to claim 1 , wherein the ...

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Номер записи: 74
28-01-2016 дата публикации

IMAGE GUIDED SURGERY WITH DYNAMIC IMAGE RECONSTRUCTION

Номер: US20160027147A1
Автор: Boyden Edward S., Hyde Roderick A., Ishikawa Muriel Y., JR. Lowell L., Leuthardt Eric C., Myhrvold Nathan P., Rivet Dennis J., Smith Michael A., Weaver Thomas A., Wood, Wood Victoria Y.H.
Принадлежит:

A method may include processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform, and applying the transform to the three-dimensional medical image to form a present image. 165.-. (canceled)66. A system , comprising:means for processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform; andmeans for applying the transform to the three-dimensional medical image to form a present image.67. A method , comprising:processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform; andapplying the transform to the three-dimensional medical image to form a present image.68. A system , comprising:circuitry for processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform; andcircuitry for applying the transform to the three-dimensional medical image to form a present image.69. The system of claim 68 , wherein circuitry for processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform comprises:selecting the three-dimensional medical image from an interior surface in three-dimensional space.70. The system of claim 68 , wherein circuitry for processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform comprises:processing two or more fiducials included in two or more current images to generate the two or more transform coefficients of the transform.71. The system of claim 68 , wherein circuitry for processing two or more ...

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Номер записи: 75
25-01-2018 дата публикации

AUTOMATIC 3D BRAIN TUMOR SEGMENTATION AND CLASSIFICATION

Номер: US20180025488A1
Автор: CHOU Chen-Rui, Liu Ming-Chang, Song Bi
Принадлежит:

A fully automatic brain tumor segmentation and classification method and system improve the healthcare experience with machine intelligence. The automatic brain tumor segmentation and classification method and system utilize whole tumor segmentation and multi-class tumor segmentation to provide accurate analysis. 1. A method programmed in a non-transitory memory of a device comprising: utilizing multi-modal MRIs including T1, T1c, Flair and T2;', 'determining a least-tumor hemisphere;', 'classifying tissue;', 'identifying intensity control points based on intensities of different tissue structures;', 'normalizing the intensity for each of the MRIs based on the intensity control points;', 'locating initial tumor seeds; and', 'segmenting a tumor based on the tumor seeds; and, 'performing whole tumor segmentation, wherein the whole tumor segmentation includesperforming multi-class tumor segmentation.2. The method of wherein the whole tumor segmentation includes: data normalization and initial segmentation.3. (canceled)4. The method of wherein the multi-class tumor segmentation includes: feature extraction claim 1 , voxel classification and refinement.5. The method of wherein feature extraction includes: determining voxel-wise features and context features claim 4 , wherein voxel-wise features include appearance features claim 4 , texture features and location features.6. The method of wherein context feature extraction further includes: extracting a mean intensity in each octant of an MRI claim 5 , extracting multiscale context features and combining the context features from a T1c MRI and a T2 MRI.7. The method of wherein voxel classification utilizes information from the feature extraction and decision trees to classify a tumor.8. The method of wherein refinement includes pathology-guided refinement to ensure a correct classification of the tumor.9. An apparatus comprising: [ utilizing multi-modal MRIs including T1, T1c, Flair and T2;', 'determining a least-tumor ...

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Номер записи: 76
25-01-2018 дата публикации

QUANTIFYING MASS EFFECT DEFORMATION WITH STRUCTURAL RADIOMICS IN BRAIN TUMOR PATIENTS

Номер: US20180025489A1
Автор: Hanson Gavin, Madabhushi Anant, Mitra Jhimli, Tiwari Pallavi
Принадлежит:

Methods and apparatus quantify mass effect deformation in diagnostic images of patients demonstrating glioblastoma multiforme (GBM). One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating GBM pathology, a delineation circuit that segments a tumor region from the image, a pre-processing circuit that generates a pre-processed image by pre-processing the segmented image, a registration circuit that registers the pre-processed image with a template image of a healthy brain, a deformation quantification circuit that computes a set of differences between a position of a brain sub-structure represented in the registered image relative to the position of the brain sub-structure represented in the template image. Embodiments may include a classification circuit that classifies the region of tissue as a long or short-term survivor based, at least in part, on the set of differences. 1. A non-transitory computer-readable storage device storing computer-executable instructions that when executed by a computer control the computer to perform a method , the method comprising:accessing a diagnostic image of a region of tissue in a patient demonstrating glioblastoma multiforme (GBM) pathology;generating a segmented diagnostic image by segmenting a tumor region from the diagnostic image;generating a pre-processed diagnostic image by pre-processing the segmented diagnostic image;generating a registered diagnostic image by registering the pre-processed diagnostic image with a healthy template; andcomputing a quantified difference between the pre-processed diagnostic image and the healthy template.2. The non-transitory computer-readable storage device of claim 1 , where the diagnostic image is a gadolinium (Gd) contrast T1w claim 1 , T2w claim 1 , or fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) image.3. The non-transitory computer-readable storage device of claim 1 , where segmenting the ...

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Номер записи: 77
10-02-2022 дата публикации

SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR DETECTING FUNCTIONAL DISORDER(S) OR AGING PROGRESSION IN MAGNETIC RESONANCE IMAGING

Номер: US20220044360A1
Автор: Guo Jia, Small Scott A.
Принадлежит:

An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium (“Gd”) enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging. 120-. (canceled)21. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for detecting at least one functional disorder or age progression of at least one patient , wherein , when a computing arrangement executes the instructions , the computing arrangement is configured to perform procedures comprising:receiving magnetic resonance imaging (MRI) information of at least one portion of the at least one patient;generating at least one gadolinium (Gd) enhanced map of the at least one portion based on the MRI information using at least one machine learning procedure; anddetecting the at least one functional disorder or age progression of the at least one patient based on the at least one Gd enhanced map.22. The computer-accessible medium of claim 21 , wherein the at least one Gd enhanced map is a full dosage Gd enhanced map.23. The computer-accessible medium of claim 22 , wherein the at least one full dosage Gd enhanced map is at least one full dosage Gd enhanced cerebral blood volume map.24. The computer- ...

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Номер записи: 78
10-02-2022 дата публикации

Deep-Learning-based T1-Enhanced Selection of Linear Coefficients (DL-TESLA) for PET/MR Attenuation Correction

Номер: US20220044399A1
Автор: An Hongyu, Chen Yasheng, Ying Chunwei
Принадлежит: WASHINGTON UNIVERSITY

Systems and methods for deep-learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation are described. 1. A system for obtaining a linear attenuation coefficient map based on at least one MR image , the system comprising a computing device with at least one processor and a non-volatile computer-readable memory , the non-volatile computer-readable memory containing a plurality of instructions executable on the at least one processor , the plurality of instructions configured to:{'sub': 1', '1', '2', '2, 'receive the at least one MR image, each MR image comprising an FATEmap, an FATEmap, and an R1 map;'}{'sub': 1', '1', '2', '2, 'transform each MR image into a pseudo-CT map using a DL-TESLA model based on the FATEmap, the FATEmap, and the R1 map; and'}transform the pseudo-CT map into the linear attenuation coefficient map using piecewise linear scaling.2. The system of claim 1 , wherein the DL-TESLA model comprising a 3D residual UNet (ResUNet).3. The system of claim 1 , wherein the piecewise linear scaling is based on empirical relationships between CT HU values and R1 values.4. The system of claim 1 , wherein the plurality of instructions is further configured to transform the pseudo-CT map into an electron density map using piecewise linear scaling.5. A computer-implemented method for obtaining a linear attenuation coefficient map based on at least one MR image claim 1 , the method comprising:{'sub': 1', '1', '2', '2, 'receiving, using the computing device, the at least one MR image, each MR image comprising an FATEmap, an FATEmap, and an R1 map;'}{'sub': 1', '1', '2', '2, 'transforming, using the computing device, each MR image into a pseudo-CT map using a DL-TESLA model based on the FATEmap, the FATEmap, and the R1 map; and'}transforming, using the computing device, the pseudo-CT map into the linear attenuation coefficient map using piecewise linear scaling.6. The method of claim 5 , wherein the DL-TESLA model ...

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Номер записи: 79
10-02-2022 дата публикации

Analyzing symmetry in image data

Номер: US20220044435A1
Автор: Diana Sima, Dirk Smeets
Принадлежит: Icometrix Nv

A method for analyzing an image to assess a degree of asymmetry in an object having a presumed mirror symmetry includes: retrieving an image of the object; obtaining a mirrored image by flipping along an axis that has an a-priori unknown spatial relation to the presumed plane of symmetry; obtaining a mapping between the retrieved image and the mirrored image; determining a measure of asymmetry in the object by considering element pairs of a first element of the retrieved image and a second element of the mirrored image according to the mapping. Obtaining the mapping comprises performing a rigid registration followed by a non-rigid registration of the retrieved image to the mirrored image. The measure of asymmetry is determined by calculating the Jacobian of the non-rigid deformation in each element of the image. The invention also pertains to a computer program product and an image processing system.

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Номер записи: 80
24-01-2019 дата публикации

Brain tumor automatic segmentation method by means of fusion of full convolutional neural network and conditional random field

Номер: US20190026897A1
Автор: Xiaomei Zhao, Yihong Wu
Принадлежит: Shenyang Institute of Automation of CAS

A method for automatic segmentation of brain tumors merging full convolution neural networks with conditional random fields. The present application intends to address the issue that presently the technology of deep learning is unable to ensure the continuity of the segmentation result in shape and in space when segmenting brain tumors. For this purpose, the present application includes the following steps: step 1, processing a magnetic resonance image comprising brain tumors by utilizing a method for non-uniformity bias correction and brightness regularization, to generate a second magnetic resonance image; step 2, performing brain tumor segmentation for said second magnetic resonance image by utilizing a neural network merging a full convolutional neural network with a conditional random field, and outputting a result of brain tumor segmentation. The present method may execute brain tumor segmentation end-to-end and slice by slice during testing, which has relatively high computation efficiency.

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Номер записи: 81
23-01-2020 дата публикации

System and method for creating registered images

Номер: US20200027264A1
Автор: Hung-Chi Tai, Kai-Lung Hua, Yu-Jen Chen
Принадлежит: MACKAY MEMORIAL HOSPITAL

Disclosed herein are a method and a system for creating a registered image that integrates the information of CT and CBCT images. With the present method and system, medical practitioners can precisely transform the information of CT image-based treatment plan into the CBCT image so as to accurately control the dosage and location of a radiation therapy. Accordingly, also disclosed herein are methods of treating a cancer in the subject with the aid of the method and/or system of the present disclosure.

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Номер записи: 82
23-01-2020 дата публикации

Methods of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification and for t-cell therapy initiation and tracking and related precision medical treatment predictive modeling platforms

Номер: US20200027561A1
Автор: Rob K. Rao
Принадлежит: SCA Robotics

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

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Номер записи: 83
28-01-2021 дата публикации

MEDICAL IMAGE PROCESSING METHOD AND APPARATUS

Номер: US20210027460A1
Автор: BEVERIDGE Erin, DIAKIDIS Georgios, MOHR Brian, SAKASHITA Naotaka, THOMSON Paul
Принадлежит: Canon Medical Systems Corporation

A medical image processing apparatus comprises processing circuitry configured to: obtain image data representative of a brain of a subject; obtain data representing a clinical sign or symptom of the subject, wherein the clinical sign or symptom is relevant to a brain condition; process the image data to obtain an estimation of an abnormality in the brain of the subject; and determine whether the estimation of the abnormality is consistent with the data representing the clinical sign or symptom. 1. A medical image processing apparatus comprising processing circuitry configured to:obtain image data representative of a brain of a subject;obtain data representing a clinical sign or symptom of the subject, wherein the clinical sign or symptom is relevant to a brain condition;process the image data to obtain an estimation of an abnormality in the brain of the subject; anddetermine whether the estimation of the abnormality is consistent with the data representing the clinical sign or symptom.2. The apparatus according to claim 1 , wherein the brain condition is stroke claim 1 , and wherein the clinical sign or symptom is indicative of a laterality of the stroke.3. The apparatus according to claim 1 , wherein the clinical sign or symptom comprises at least one of a behavior of a body part claim 1 , a position of a body part claim 1 , an orientation of a body part claim 1 , a motion of a body part.4. The apparatus according to claim 1 , wherein the clinical sign or symptom comprises at least one of an eye gaze direction claim 1 , an angle of eye gaze claim 1 , an asymmetric muscle tension claim 1 , an angle of head on trunk claim 1 , a face asymmetry claim 1 , a tongue deviation.5. The apparatus according to claim 1 , wherein the clinical sign or symptom comprises sustained eye deviation.6. The apparatus according to claim 1 , wherein the clinical sign or symptom comprises an eye deviation occurring in two or more scans and/or at two or more times.7. The apparatus according ...

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Номер записи: 84
31-01-2019 дата публикации

METHOD FOR DETERMINING ISCHEMIC STATUS OR ASSESSING STROKE ONSET TIME OF A BRAIN REGION

Номер: US20190029557A1
Автор: CHEN Cheng-Yu, CHUNG Hsiao-Wen, KAO Yu-Chieh Jill, KUO Duen-Pang, LU Chia-Feng
Принадлежит:

The invention relates to a method for determining ischemic status. The method comprises acquiring magnetic resonance diffusion tensor matrices and obtaining a relative decrease of diffusion magnitude due to the ischemic status from the magnetic resonance diffusion tensor matrices. The invention also relates to a method for assessing stroke onset time. The method comprises acquiring magnetic resonance diffusion tensor matrices and obtaining a relative decrease of pure anisotropy due to stroke from the magnetic resonance diffusion tensor matrices. 1. A method for determining ischemic status of a brain region , comprising:acquiring magnetic resonance diffusion tensor matrices of the brain region and of a normal brain tissue, wherein the normal brain tissue and the brain region belong to the same brain tissue type;{'sub': i', 'n, 'obtaining diffusion magnitude (L) of the brain region (L) and diffusion magnitude of the normal brain tissue (L) from the magnetic resonance diffusion tensor matrices; and'}{'sub': i', 'n', 'i', 'n, 'calculating a relative decrease of Lto L, and the ischemic status is determined by the relative decrease of Lto L.'}2. The method according to claim 1 , which further comprises acquiring a brain tissue type map claim 1 , wherein the brain tissue type map is obtained by fractional anisotropy (FA) mapping calculated from the magnetic resonance diffusion tensor matrices.3. The method according to claim 1 , wherein the brain region and the normal brain tissue belong to the same brain tissue type in an atlas-based tissue classification method.4. The method according to claim 1 , wherein the normal brain tissue is a contralateral homologous tissue of the brain region.5. A method for establishing an index of given ischemic status claim 1 , comprising:{'claim-ref': {'@idref': 'CLM-00001', 'claim 1'}, 'sub': i', 'n', 'i', 'n, 'determining ischemic status of a brain region having the given ischemic status according to the method as claimed in for obtaining ...

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Номер записи: 85
30-01-2020 дата публикации

METHOD AND SYSTEM FOR DETECTING AND IDENTIFYING ACUTE STRESS RESPONSE FROM TRAUMATIC EXPOSURE, ITS TRANSITION TO POST TRAUMATIC STRESS DISORDER, AND MONITORING SUBSEQUENT THERAPY

Номер: US20200029816A1
Автор: Mountford Carolyn
Принадлежит: Translational Research Institute Pty Ltd as trustee for Translational Research Institute Trust

The present invention relates to a method and system for using neurochemical markers to enable whether a subject is experiencing acute stress, trauma, or PTSD and providing the capacity to monitor response to therapy on an individual basis. The markers can be an increase of NAA, glutamine or Fuc IV and lactate and a decrease of Fuc IV during the transition from acute stress to PTSD. 1. A method for enabling detection of whether a subject is experiencing acute stress , trauma-exposure or PTSD , comprising:obtaining MR spectral data from a subject's brain tissue using a MR spectroscopy device; andproducing, from the MR spectra obtained, spectral data which enables the detection of whether the subject is experiencing acute stress, trauma-exposure or PTSD by detecting the presence of at least one neurochemical marker, and comparing the detected amount of neurochemical marker with reference amounts for healthy persons.2. The method of claim 1 , wherein the MR data is obtained using 2D COSY.3. The method of claim 1 , wherein an increase of at least one of N-Acetylaspartate and glutamate enables the detection of whether the subject is experiencing acute stress or trauma-exposure.4. The method of claim 1 , wherein an increase of Fuc IV and lactate enables the detection of whether the subject is experiencing PTSD.5. The method of claim 1 , wherein an increase of Fuc IV and lactate claim 1 , and a decrease of Fuc VI claim 1 , enables the detection of whether the subject is experiencing acute stress claim 1 , trauma-exposure or PTSD.6. The method of claim 1 , including treating the patient with a treatment protocol to mitigate acute stress claim 1 , trauma-exposure claim 1 , or PTSD.7. The method of claim 6 , where in the treatment protocol includes physiotherapy.8. The method of claim 1 , wherein the steps of obtaining and producing are repeated after a time interval to monitor the progress of a treatment protocol.9. The method of claim 8 , wherein the time interval is about ...

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Номер записи: 86
30-01-2020 дата публикации

PHYSIOLOGICAL STATE DETERMINATION DEVICE

Номер: US20200029884A1
Автор: ARAI Junichiro, GOTOU Takashi, HASHIMOTO Arina, HASHIZUME Hideki, HIRAYAMA Takahiro, KOTANI Yasunori, MATSUBARA AKIRA, OHGAMI Yoshimi, TOMATSU Taro
Принадлежит:

A physiological state determination device determines a predetermined physiological state of a subject. The physiological state determination device includes a brain function activation information detection unit, a face change information acquisition unit, and a physiological state determination unit. The brain function activation information detection unit detects brain function activation information corresponding to a physiological state. The face change information acquisition unit acquires face change information indicating a time-series change in face data of a subject. The physiological state determination unit determines the predetermined physiological state of the subject based on the brain function activation information and the face change information. 1. A physiological state determination device for determining a predetermined physiological state of a subject , the physiological state determination device comprising:a brain function activation information detection unit configured to detect brain function activation information corresponding to the physiological state;a face change information acquisition unit configured to acquire face change information indicating a time-series change in face data of the subject; anda physiological state determination unit configured to determine the predetermined physiological state of the subject based on the brain function activation information and the face change information.2. The physiological state determination device according to claim 1 , wherein a specific operation detection unit that, when a specific operation is performed on a predetermined device by the subject or a measuring person other than the subject, determines that a brain function activation stimulus is provided to the subject and detects the brain function activation information, and', 'a specific environment detection unit that, when state information in a predetermined environment is state information for a specific environment in which a ...

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Номер записи: 87
30-01-2020 дата публикации

System And Method For Normalizing Standarized Uptake Values In Brain Positron Emission Tomography (PET) Images

Номер: US20200029918A1
Автор: Chen Jingyun, de Leon Mony, Li Yi
Принадлежит:

Systems and methods are for analyzing Positron Emission Tomography (PET) image data. The methods may include generating a set of standardized uptake values (SUVs) of global or localized PET data for voxels within a selected region of interest (ROI), normalizing the set of SUVs by generating a set of SUVPs where each corresponding SUVP for each SUV is obtained using the formula: SUVP=(SUV−M)/S, wherein M corresponds to a peak value for the set of SUVs, and S corresponds to a spread for the set of SUVs, and generating a normalized image based on the set of SUVPs for the ROI. The systems may include any suitable device for PET image analysis performing the methods. 1. A method for analyzing Positron Emission Tomography (PET) image data comprising:localizing the PET data with at least one image mask to generate localized PET data, wherein the PET data is co-registered to correspond to anatomical structures represented by the image mask;generating a set of image intensity values (Ps) based on the localized PET data for voxels within a selected region of interest (ROI), each of the image intensity values corresponding to an intensity of the PET data for a corresponding one of the voxels; [{'br': None, 'i': N', 'P−M', 'S,, '=()/'}, M corresponds to a peak value for the set of image intensity values (Ps), and', 'S corresponds to a spread for the set of image intensity values (Ps); and, 'wherein'}], 'normalizing the set of image intensity values (Ps) by generating a set of normalized values (Ns) where each corresponding N for each P is obtained using the formulagenerating a normalized image based on the set of normalized values (Ns).2. The method of claim 1 , further comprising fitting a Gaussian curve to a histogram of the set of image intensity values (Ps) claim 1 , wherein M is a mean of the fitted Gaussian curve claim 1 , and S is a standard deviation of the fitted Gaussian curve.3. The method of claim 1 , wherein the ROI corresponds to a predetermined portion of a ...

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Номер записи: 88
30-01-2020 дата публикации

Modeling a collapsed lung using ct data

Номер: US20200030033A1
Автор: Arlen K. Ward, Francesca Rossetto, Joe D. Sartor, Ramesh Raghupathy, Srikara V. Peelukhana
Принадлежит: COVIDIEN LP

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

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Номер записи: 89
02-02-2017 дата публикации

SYSTEMS AND METHODS FOR AUTOMATED VOXELATION OF REGIONS OF INTEREST FOR MAGNETIC RESONANCE SPECTROSCOPY

Номер: US20170032209A1
Автор: Claude John Patrick, III James C., Kane Paul H., Peacock
Принадлежит:

A system and method for automating an appropriate voxel prescription in a uniquely definable region of interest (ROI) in a tissue of a patient is provided, such as for purpose of conducting magnetic resonance spectroscopy (MRS) in the ROI. The dimensions and coordinates of a single three dimensional rectilinear volume (voxel) within a single region of interest (ROI) are automatically identified. This is done, in some embodiments by: (1) applying statistically identified ROI search areas within a field of view (FOV); (2) image processing an MRI image to smooth the background and enhance a particular structure useful to define the ROI; (3) identifying a population of pixels that define the particular structure; (4) performing a statistical analysis of the pixel population to fit a 2D model such as an ellipsoid to the population and subsequently fit a rectilinear shape within the model; (5) repetiting elements (1) through (4) using multiple images that encompass the 3D ROI to create a 3D rectilinear shape; (6) a repetition of elements (1) through (5) for multiple ROIs with a common FOV. A manual interface may also be provided, allowing for override to replace by manual prescription, assistance to identify structures (e.g. clicking on disc levels), or modifying the automated voxel (e.g. modify location, shape, or one or more dimensions). 1220.-. (canceled)221. A method for obtaining information relating to a region of interest , the method comprising:accessing a plurality of electronic magnetic resonance imaging (MRI) images of an area that includes a region of interest in an intervertebral disc of a spine; automatically processing the electronic MRI image, using one or more computer processors, to emphasize pixels associated with two opposite borders between the intervertebral disc and two superiorly and inferiorly adjacent vertebral bodies, respectively;', 'automatically identifying, using the one or more computer processors, a population of pixels in the electronic ...

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Номер записи: 90
02-02-2017 дата публикации

COMPUTER AIDED DIAGNOSTIC SYSTEM FOR MAPPING OF BRAIN IMAGES

Номер: US20170032520A1
Автор: Casanova Manuel F., El-Baz Ayman S., Nitzken Matthew J.
Принадлежит:

Systems, methods, and computer program products for classifying a brain are disclosed. An embodiment method includes processing image data to generate segmented image data of a brain cortex. The method further includes generating a statistical analysis of the brain based on a three dimensional (3D) model of the brain cortex generated from the segmented image data. The method further includes using the statistical analysis to classify the brain cortex and to identify the brain as being associated with a particular neurological condition. According to a further embodiment, generating the 3D model of the brain further includes registering a 3D volume associated with the model with a corresponding reference volume and generating a 3D mesh associated with the registered 3D volume. The method further includes generating the statistical analysis by analyzing individual mesh nodes of the registered 3D mesh based on a spherical harmonic shape analysis of the 3D model. 1. A processor implemented method for classifying a brain , the method comprising:using at least one processor to process image data including a brain cortex to generate segmented image data for the brain cortex;generating a three dimensional (3D) model of the brain cortex based on the segmented image data, wherein the 3D model comprises a statistical analysis of the brain; andclassifying the brain cortex based on the statistical analysis.2. The method of claim 1 , further comprising identifying characteristics of localized regions of the brain based on the statistical analysis.3. The method of claim 2 , further comprising identifying the localized regions as Brodmann areas.4. The method of claim 3 , further comprising:comparing the identified characteristics of the Brodmann areas with characteristics of corresponding Brodmann areas of a reference brain to determine localized differences between the brain and the reference brain; andclassifying the brain cortex based on the localized differences.5. The method ...

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Номер записи: 91
04-02-2016 дата публикации

METHOD FOR DETECTING AND QUANTIFYING CEREBRAL INFARCT

Номер: US20160035085A1
Автор: CHEN Yu-Wei, PENG Syu-Jyun, TSAI Jang-Zern, WANG Kuo-Wei
Принадлежит:

A method for detecting a cerebral infarct includes receiving an image of a brain of a subject from a magnetic resonance imaging scanner, wherein the image has a plurality of voxels, and each of the voxels has a voxel intensity. Then, the voxel intensities are normalized, wherein the normalized voxel intensities have a distribution peak, and the normalized voxel intensity of the distribution peak is I. A threshold is determined, which is the I+ a value. Voxel having the normalized voxel intensity larger than the threshold is selected, wherein the selected voxel is the cerebral infarct. A method for quantifying the cerebral infarct is also provided. 1. A method for detecting a cerebral infarct , comprising:receiving an image of a brain of a subject from a magnetic resonance imaging scanner, wherein the image has a plurality of voxels, and each of the voxels has a voxel intensity;{'sub': 'peak', 'normalizing the voxel intensities to make the voxel intensities disperse in a standard range, wherein the normalized voxel intensities have a distribution peak, and the normalized voxel intensity of the distribution peak is I;'}{'sub': peak', 'peak, 'determining a threshold, which is the I+ a value, wherein the value is a difference value between a minimum normalized voxel intensity of the cerebral infarct diagnosed by a semi-automatic segmentation method and the I; and'}selecting voxel having the normalized voxel intensity larger than the threshold, wherein the selected voxel is the cerebral infarct.2. The method of claim 1 , wherein receiving the image of the brain of the subject from the magnetic resonance imaging scanner comprises determining a brain mask of the subject in the image.3. The method of wherein the brain mask comprises an inner surface and an outer surface of a skull of the subject.4. The method of claim 1 , wherein the image is obtained by diffusion-weighted imaging (DWI).5. The method of claim 1 , wherein the standard range is (0 claim 1 , 1).6. The method ...

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Номер записи: 92
04-02-2016 дата публикации

MULTI MODALITY BRAIN MAPPING SYSTEM (MBMS) USING ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION

Номер: US20160035093A1
Автор: Kateb Babak, Nikzad Shouleh
Принадлежит:

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image. 1. A system , comprising one or more scopes coupled to one or more processors , wherein: obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified;', 'receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and', 'generate, using machine learning trained using the training data, one or more viewable indicators identifying one or more abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed; and, 'the one or more processorsone or more of the scopes display the one or more viewable indicators on the second image.2. The system of claim 1 , wherein:one or more of the processors comprise one or more multi-modality data processors; andthe multi-modality data processors register at least two of the first images and/or first data obtained from biopsy, Infrared Imaging, Ultraviolet Imaging, Diffusion Tensor Imaging (DTI), Computed ...

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Номер записи: 93
01-02-2018 дата публикации

ANOMALY DETECTION IN VOLUMETRIC IMAGES

Номер: US20180033144A1
Автор: Chen Sea, Risman Alexander
Принадлежит:

Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image. 119-. (canceled)20. A computer-implemented method for detecting anomalies in volumetric medical images , each volumetric image comprising a spaced set of two-dimensional slice images , the method comprising:training an initial computer-implemented two-dimensional regular convolutional neural network (CNN) using a training set of slice images with a first set of slice-level labels; and the RNN input receives volumetric images with slices encoded by a modified CNN; and', 'the modified CNN comprises the initial CNN having an output layer removed, leaving a last dense layer prior to the removed output layer as a new output layer, such that each encoded slice has the dimension of the new output layer., 'training a recurrent neural network (RNN) using a training set of encoded volumetric images and a corresponding second set of labels, in which21. The method of claim 20 , in which the first set of slice-level labels comprise a binary indicator of whether a slice contains evidence of a disease.22. The method of claim 20 , in which the first set of slice-level labels comprise a segmentation mask marking evidence of a disease in a slice.23. The method of claim 20 , in which the RNN is a Long Short-Term Memory RNN.24. The method of claim 20 , in which the second set of labels comprises a single series-level label.25. The method of claim 24 , in which the single series-level label comprises an indication of ...

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Номер записи: 94
31-01-2019 дата публикации

System and Method for Optimized Diffusion - Weighted Imaging

Номер: US20190033410A1
Автор: Aliotta Eric, Ennis Daniel B., Wu Holden H.
Принадлежит:

A system and method for optimized diffusion-weighted imaging is provided. In one aspect, the method includes providing a plurality of constraints for imaging a target at a selected diffusion weighting, and applying an optimization framework to generate an optimized diffusion encoding gradient waveform satisfying the plurality of constraints. The method also includes performing, using the MRI system, a pulse sequence comprising the optimized diffusion encoding gradient waveform to generate diffusion-weighted data, and generating at least one image of the target using the diffusion-weighted data. 1. A method for generating images using a magnetic resonance imaging (“MRI”) system , the method comprising:a) providing a plurality of constraints for imaging a target at a selected diffusion weighting;b) applying an optimization framework to generate an optimized diffusion encoding gradient waveform satisfying the plurality of constraints;c) performing, using the Mill system, a pulse sequence comprising the optimized diffusion encoding gradient waveform to generate diffusion-weighted data; andd) generating at least one image of the target using the diffusion-weighted data.2. The method of claim 1 , wherein the plurality of constraints comprises at least one of at least one of gradient constraints claim 1 , gradient moment constraints claim 1 , and hardware constraints.3. The method of claim 2 , wherein the gradient constraints comprise zero gradient values during a radiofrequency (“RF”) activity and a readout.4. The method of claim 2 , wherein the gradient moment constraints comprise at least one of a zero moment (“M0”) claim 2 , a first moment (“M1”) claim 2 , a second moment (“M2”) claim 2 , a third moment (“M3”) and a fourth moment (“M4”) is nulled.5. The method of claim 1 , wherein applying the optimization framework at step b) further comprises performing an iterative process to minimize at least one of timing parameters comprising a gradient duration during a ...

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Номер записи: 95
31-01-2019 дата публикации

SYSTEMS AND METHODS FOR AUTOMATED DETECTION IN MAGNETIC RESONANCE IMAGES

Номер: US20190033414A1
Автор: Charvat Gregory L., Ralston Tyler S., Rothberg Jonathan M., Sofka Michal
Принадлежит:

Some aspects include a method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device. The method comprises, while the patient remains positioned within the low-field MRI device, acquiring first and second magnetic resonance (MR) image data of the patient's brain; providing the first and second MR image data as input to a trained statistical classifier to obtain corresponding first and second output; identifying, using the first output, at least one initial value of at least one feature indicative of a size of the abnormality; identifying, using the second output, at least one updated value of the at least one feature; determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature. 1. A method of determining change in size of an abnormality in a brain of a patient positioned within a low-field magnetic resonance imaging (MRI) device , the method comprising: ["acquiring first magnetic resonance (MR) image data of a patient's brain;", 'providing the first MR image data as input to a trained statistical classifier to obtain corresponding first output;', "identifying, using the first output, at least one initial value of at least one feature indicative of a size of an abnormality in the patient's brain;", "acquiring second MR image data of the patient's brain subsequent to acquiring the first MR image data;", 'providing the second MR image data as input to the trained statistical classifier to obtain corresponding second output;', "identifying, using the second output, at least one updated value of the at least one feature indicative of the size of the abnormality in the patient's brain;", 'determining the change in the size of the abnormality using the at least one initial value of the at least one feature and the at least one updated value of the at least one feature ...

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Номер записи: 96
31-01-2019 дата публикации

SYSTEMS AND METHODS FOR AUTOMATED DETECTION IN MAGNETIC RESONANCE IMAGES

Номер: US20190033415A1
Автор: Charvat Gregory L., Ralston Tyler S., Rothberg Jonathan M., Sofka Michal
Принадлежит:

Some aspects include a method of detecting change in biological subject matter of a patient positioned within a low-field magnetic resonance imaging device, the method comprising: while the patient remains positioned within the low-field magnetic resonance device: acquiring first magnetic resonance image data of a portion of the patient; acquiring second magnetic resonance image data of the portion of the patient subsequent to acquiring the first magnetic resonance image data; aligning the first magnetic resonance image data and the second magnetic resonance image data; and comparing the aligned first magnetic resonance image data and second magnetic resonance image data to detect at least one change in the biological subject matter of the portion of the patient. 1. A method of detecting change in biological subject matter of a patient positioned within a low-field magnetic resonance imaging (MRI) device , the method comprising: acquiring first magnetic resonance image data of a portion of the patient;', 'acquiring second magnetic resonance image data of the portion of the patient subsequent to acquiring the first magnetic resonance image data;', 'aligning the first magnetic resonance image data and the second magnetic resonance image data; and', 'comparing the aligned first magnetic resonance image data and second magnetic resonance image data to detect at least one change in the biological subject matter of the portion of the patient., 'while the patient remains positioned within the low-field MRI device2. The method of claim 1 , further comprising modifying at least one acquisition parameter based on the at least one change in the biological subject matter of the portion of the patient.3. The method of claim 2 , further comprising acquiring third magnetic resonance image data of the portion of the patient using the modified at least one acquisition parameter.4. The method of claim 3 , wherein the at least one acquisition parameter is modified so to change at ...

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Номер записи: 97
05-02-2015 дата публикации

METHOD, APPARATUS AND SYSTEM FOR LOCALIZING A SPINE

Номер: US20150036909A1
Автор: BÜHLER Katja, HLADUVKA Jiri, Major David
Принадлежит:

A method and a corresponding apparatus and system localizes a spine in an image, in particular a computed tomography (CT) image, of a human or animal body, allowing for a reduced need for computational power and/or memory on the one hand and assuring a reliable localization of the spine on the other hand. The method includes a) acquiring a plurality of slice images of at least a part of a human or animal body, and b) automatically selecting slice images and/or parts of slice images from the acquired plurality of slice images by considering at least one parameter (μ, ν, σ, Λ) characterizing a distribution of bones in the acquired slice images, wherein the selected slice images and/or parts of the slice images includes image information about the spine. 115-. (canceled)16. A method for localizing a spine in one of an image and a computed tomography image , of a human or animal body , the method comprising the following steps:a) acquiring a plurality of slice images of at least a part of a human or animal body; andb) automatically selecting slice images and/or portions of slice images from the acquired plurality of slice images by considering at least one parameter characterizing a distribution of bones in the acquired plurality of slice images; whereinthe selected slice images and/or portions of the slice images include image information about the spine.17. The method according to claim 16 , wherein the at least one parameter relates to a center of gravity of pixels relating to bones in the acquired plurality of slice images.18. The method according to claim 16 , wherein the at least one parameter represents a refined center of gravity of pixels relating to bones within a refinement window in the acquired plurality of slice images claim 16 , and the refinement window spans around an original center of gravity of pixels relating to the bones in the acquired plurality of slice images.19. The method according to claim 18 , wherein the selected slice images and/or ...

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Номер записи: 98
17-02-2022 дата публикации

CROSS MODALITY TRAINING OF MACHINE LEARNING MODELS

Номер: US20220051396A1
Автор: Bar Amir, MUHAMEDRAHIMOV Raouf, WITIES Rachel
Принадлежит: Zebra Medical Vision Ltd.

There is provided a method, comprising: providing a training dataset including, medical images and corresponding text based reports, and concurrently training a natural language processing (NLP) machine learning (ML) model for generating a NLP category for a target text based report and a visual ML model for generating a visual finding for a target image, by: training the NLP ML model using the text based reports of the training dataset and a ground truth comprising the visual finding generated by the visual ML model in response to an input of the images corresponding to the text based reports of the training dataset, and training the visual ML model using the images of the training dataset and a ground truth comprising the NLP category generated by the NLP ML model in response to an input of the text based reports corresponding to the images of the training dataset. 1. A computer implemented method for training a visual machine learning (ML) model component and a natural language processing (NLP) ML model component , comprising:providing a training dataset including, for each of a plurality of sample individuals, a medical image and a corresponding text based report;providing the NLP ML model component for generating an outcome of at least one NLP category in response to an input of a target text based report;providing the visual ML model component for generating an outcome of at least one visual finding in response to an input of a target image; and{'claim-text': ['training the NLP ML model using an input of the text based reports of the training dataset and a ground truth comprising the outcome of the at least one visual finding generated by the visual ML model in response to an input of the images corresponding to the text based reports of the training dataset;', 'training the visual ML model using an input of the images of the training dataset and a ground truth comprising the outcome of the at least one NLP category generated by the NLP ML model in response to ...

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Номер записи: 99
17-02-2022 дата публикации

SYSTEMS, METHODS, AND MEDIA FOR AUTOMATICALLY TRANSFORMING A DIGITAL IMAGE INTO A SIMULATED PATHOLOGY IMAGE

Номер: US20220051400A1
Автор: Belykh Evgenii, Izadyyazdanabadi Mohammadhassan, Preul Mark C., YANG Yezhou
Принадлежит:

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for automatically transforming a digital image into a simulated pathology image are provided. In some embodiments, the method comprises: receiving a content image from an endomicroscopy device; receiving, from a hidden layer of a convolutional neural network (CNN) trained to recognize a multitude of classes of common objects, features indicative of content of the content image; receiving, providing a style reference image to the CNN; receiving, from another hidden layer of the CNN, features indicative of a style of the style reference image; receiving, from the hidden layers of the CNN, features indicative of content and style of a target image; generating a loss value based on the features of the content image, the style reference image, and the target image; minimizing the loss value; and displaying the target image with the minimized loss. 116-. (canceled)17. A method for transforming a digital image generated by an endomicroscopy device into a simulated pathology image , the method comprising:(a) receiving a first image depicting in vivo tissue of a first subject;(b) generating a first plurality of features indicative of content of the first image using a first hidden layer of a pre-trained convolutional neural network trained to recognize at least a multitude of classes of common objects; 'wherein the second image depicts a histopathology slide prepared using tissue of a second subject;', '(c) receiving a second plurality of features indicative of style of a second image corresponding to features generated using a second hidden layer of the pre-trained convolutional neural network,'}(d) generating a third image;(e) generating a third plurality of features indicative of content of the third image using the first hidden layer;(f) generating a fourth plurality of features indicative of a style of the third image using the second hidden layer;(g) generating a loss value ...

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Номер записи: 100