RADIOTHERAPY TREATMENT PLANS USING DIFFERENTIABLE DOSE FUNCTIONS

Techniques for generating a radiotherapy treatment plan parameter are provided. The techniques include receiving radiotherapy treatment plan information; processing the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation; and generating a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters. 1. A method for generating a radiotherapy treatment plan parameter , the method comprising:receiving, by processor circuitry, radiotherapy treatment plan information;processing, by the processor circuitry, the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation, wherein the derivative of the dose calculation is used in an optimization process or a machine learning model that is based on a loss function, wherein the derivative of the dose calculation is computed with respect to at least one of one or more radiation parameters or one or more geometry parameters of a radiotherapy treatment device; andgenerating, by the processor circuitry, a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters.2. The method of claim 1 , wherein the radiotherapy treatment plan information includes at least one of a magnetic resonance (MR) image claim 1 , a cone-beam computed tomography (CBCT) image claim 1 , a computed tomography (CT) image claim 1 , a dose distribution claim 1 , a segmentation map and a distance map.3. The method of claim 2 , wherein the estimated one or more radiotherapy treatment plan parameters comprises at least one of a synthetic computed tomography (sCT) image and a dose distribution.4. The method of claim 1 , wherein processing the radiotherapy treatment plan ...

Compressing radiotherapy treatment plan optimization problems

Techniques for solving a radiotherapy treatment plan optimization problem are provided. The techniques include receiving a first radiotherapy treatment plan optimization problem having a first set of parameters; processing the first set of parameters to estimate a second set of parameters of a second radiotherapy treatment plan optimization problem; generating a solution to the second radiotherapy treatment plan optimization problem based on the estimated second set of parameters; and generating a radiotherapy treatment plan based on the solution to the second radiotherapy treatment plan optimization problem.

RADIOTHERAPY TREATMENT PLAN OPTIMIZATION USING MACHINE LEARNING

Techniques for solving a radiotherapy treatment plan optimization problem are provided. The techniques include receiving a radiotherapy treatment plan optimization problem; processing the radiotherapy treatment plan optimization problem with a machine learning model to estimate one or more optimization variables of the radiotherapy treatment plan optimization problem, wherein the machine learning model is trained to establish a relationship between the one or more optimization variables and parameters of a plurality of training radiotherapy treatment plan optimization problems; and generating a solution to the radiotherapy treatment plan optimization problem based on the estimated one or more optimization variables of the radiotherapy treatment plan optimization problem. 1. A method for solving a radiotherapy treatment plan optimization problem , the method comprising:receiving, by processor circuitry, a radiotherapy treatment plan optimization problem;processing, by the processor circuitry, the radiotherapy treatment plan optimization problem with a machine learning model to estimate one or more optimization variables of the radiotherapy treatment plan optimization problem, wherein the machine learning model is trained to establish a relationship between the one or more optimization variables and parameters of a plurality of training radiotherapy treatment plan optimization problems, wherein the machine learning model includes a deep neural network; andgenerating, by the processor circuitry, a solution to the radiotherapy treatment plan optimization problem based on the estimated one or more optimization variables of the radiotherapy treatment plan optimization problem, wherein the solution comprises at least one of radiotherapy device parameters, fluence maps, isocenter locations, beam-angles or beam-on times.2. The method of claim 1 , wherein the machine learning model comprises one or more intermediate estimated optimization variables used in a subsequent ...

METHOD AND SYSTEM FOR CALIBRATION

A method of calibrating a positioning system in a radiation therapy system is disclosed, including a radiation therapy unit having a fixed radiation focus. The method includes the steps of irradiating a calibration tool including at least one reference object, capturing at least one two-dimensional image including cross-sectional representations of reference objects of the calibration tool and determining image coordinates of the representation of each reference object. Based on the reference objects image coordinates, positions of the reference objects in the stereotactic coordinate system relative to an origin of the calibration tool and the position of the origin of the calibration tool relative to the imaging unit, a position difference between the position of the calibration tool in the stereotactic coordinate system and a position of the calibration tool in an imaging system coordinate system including a translational and rotational position difference is calculated. 118.-. (canceled)19. A method for calibrating an imaging system capturing images of a patient in relation to a radiation therapy system , which radiation therapy system comprises a radiation therapy unit having a fixed radiation focus , and a positioning system for positioning a patient in relation to the fixed focus in the radiation therapy unit , the method comprising the steps of:irradiating a calibration tool comprising at least one reference object with ionizing radiation during an image scanning procedure using a radiation unit of the imaging system, wherein the calibration tool and the at least one reference object have known positions in the stereotactic coordinate system;capturing at least one two-dimensional image including cross-sectional representations of reference objects of the calibration tool using a detector of the imaging system during the image scanning procedure;{'sub': 'xy', 'determining image coordinates, d, of the representation of each reference object in the captured ...

PARAMETER SEARCH IN RADIOTHERAPY TREATMENT PLAN OPTIMIZATION

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan. 1. A method for generating a radiotherapy treatment plan , the method comprising:receiving, by processor circuitry, a radiotherapy optimization problem, the radiotherapy optimization problem comprising a plurality of parameters;processing the radiotherapy optimization problem to instantiate a first set of candidate parameters;converting the first set of candidate parameters into an adapted representation;defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted radiotherapy optimization problem estimates a solution to the radiotherapy optimization problem;processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; andprocessing the estimated function of the solution to the adapted radiotherapy optimization problem to generate a deliverable radiotherapy treatment plan.2. The method of claim 1 , wherein instantiating the first set of candidate parameters comprises a sampling process that generates candidate parameters ...

RADIOTHERAPY PLANNING SYSTEMS

Embodiments of the present disclosure are directed to treatment planning systems for a radiotherapy apparatus. In one implementation, a treatment planning system may include a computational processor configured to apply a set of instructions to an input data set. The input data set may include a three-dimensional dose distribution for delivery by the radiotherapy apparatus to a volume to be irradiated, a three-dimensional volume image characterizing tissue types within the volume to be irradiated, and a set of apparatus parameters which characterize the radiotherapy apparatus. The set of instructions may include a computational process configured to output a treatment plan for delivery of the dose distribution by the radiotherapy apparatus, by optimising a function representing a time-dependent response of the tissue types to an applied radiation. 1. A treatment planning system for a radiotherapy apparatus , the treatment planning system comprising: a dose distribution for a volume to be irradiated, wherein the dose distribution is configured to be delivered by the radiotherapy apparatus,', 'a three-dimensional volume image characterizing tissue types within the volume to be irradiated, and', 'a set of process parameters which characterize a radiotherapy delivery process;, 'a computational processor configured to apply a set of instructions to an input data set, the input data set includingthe set of instructions including a computational process configured to output a treatment plan for delivery of the dose distribution when implemented by the radiotherapy delivery process, wherein the computational process optimizes a function representing a time-dependent response of the tissue types to an applied radiation dose.2. The treatment planning system of claim 1 , wherein:the input data set includes at least one treatment fraction over which the dose distribution is configured to be delivered;the set of instructions includes a fractional distribution step configured to ...

METHODS FOR INVERSE PLANNING

In the field of radiotherapy, methods for dose or treatment planning for a radiotherapy system including a radiotherapy unit are described. A spatial dose delivered can be changed by adjusting beam shape settings, wherein delivered radiation is determined using an optimization problem that steers the delivered radiation according to objectives reflecting criteria for regions of interest including at least one of: targets to be treated during treatment of the patient, organs at risk and/or healthy tissue. The method includes determining an inner set of voxels and providing a first frame description for the inner set of voxels, where the first frame description reflects criteria for the inner set of voxels. Determining an outer set of voxels encompassing the target volume and the inner set of voxels and a frame description for the outer set of voxels is provided where each reflecting criteria for the outer set of voxels. The frame descriptions are then used in the optimization problem that steers the delivered radiation. 1. A method for treatment planning for a radiotherapy , wherein delivered radiation is determined using an optimization problem that steers the delivered radiation according to objectives reflecting criteria for regions of interest , that include at least one of: at least one target to be treated during treatment of the patient , organs at risk and/or healthy tissue; said method comprising the steps of:determining an inner set of voxels that encompasses the outer surface of a target volume;providing a first frame description for an inner set of voxels, said first frame description reflecting at least one criterion for the inner set of voxels;determining at least one outer set of voxels encompassing said target volume and said inner set of voxels;providing a respective frame description for each outer set of voxels, each frame description reflecting at least one criterion for that outer set of voxels; andusing said frame descriptions in the ...

METHODS FOR INVERSE PLANNING

In the field of radiotherapy, methods for dose or treatment planning for a radiotherapy system are disclosed, wherein a spatial dose delivered can be adjusted and delivered radiation is determined using an optimization problem that steers the delivered radiation according to a frame description reflecting criteria for regions of interest that include at least one of targets to be treated during treatment of the patient, organs at risk and/or healthy tissue. The method includes estimating a voxel set receiving a higher dose than a predetermined threshold dose level, which voxel set includes voxels from at least one target volume. Further, a low dose voxel set is determined and a frame description for the voxels in the low dose voxel set is provided where voxels receiving a dose exceeding a predetermined threshold value is penalized such that the dose delivered to the low dose voxel set is suppressed. The frame description is then used in the optimization problem that steers the delivered radiation. 1. A method for treatment planning in radiotherapy , wherein delivered radiation is determined using an optimization problem that steers the delivered radiation according to objectives reflecting criteria for regions of interest , that include at least one of: at least one target to be treated during treatment of the patient , organs at risk and/or healthy tissue , said method comprising the steps of:estimating a voxel set receiving a higher dose than a predetermined threshold dose level, said voxel set including voxels from at least one target volume;determining a low dose voxel set;providing a frame description for the voxels in the low dose voxel set, wherein according to the frame description, the dose delivered to the low dose voxel set is suppressed; andusing said frame description in the optimization problem that steers the delivered radiation.2. A method for treatment planning for a radiotherapy system , the radiotherapy system comprising a radiotherapy unit , ...

COMPUTING RADIOTHERAPY DOSE DISTRIBUTION

Systems and methods for calculating radiotherapy dose distribution are provided. The systems and methods include operations for receiving data representing at least one of particle trajectories or a dose deposition pattern in a simulated delivery of a radiotherapy plan; applying a dose calculation process to the received data to generate a first radiotherapy dose distribution having a first level of detail; and processing the first radiotherapy dose distribution using a trained machine learning technique to generate a second radiotherapy dose distribution having a second level of detail that enhances the first level of detail. 1. A computer implemented method for calculating radiotherapy dose distribution , the method comprising:receiving data representing at least one of particle trajectories or a dose deposition pattern in a simulated delivery of a radiotherapy plan;applying a dose calculation process to the received data to generate a first radiotherapy dose distribution having a first level of detail; andprocessing the first radiotherapy dose distribution using a trained machine learning technique to generate a second radiotherapy dose distribution having a second level of detail that enhances the first level of detail.2. The method of claim 1 , further comprising generating a radiation dose distribution or treatment plan to perform an operation associated with a radiotherapy treatment device based on the second radiotherapy dose distribution claim 1 , wherein the operation includes at least one of delivering radiotherapy using the radiotherapy treatment device or verifying proper operation of the radiotherapy treatment device in real-time during treatment.3. The method of claim 1 , wherein the particle includes at least one of a photon claim 1 , proton claim 1 , electron or ion claim 1 , and wherein the dose calculation process applied to generate the first radiotherapy dose distribution includes at least one or a combination of a Monte Carlo simulation or a ...

GENERATION OF REALIZABLE RADIOTHERAPY PLANS

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving an input parameter related to a patient, the input parameter being of a given type; processing the input parameter with a machine learning technique to estimate a realizable plan parameter of a radiotherapy treatment plan, wherein the machine learning technique is trained to establish a relationship between the given type of input parameter and a set of realizable radiotherapy treatment plan parameters to achieve a target radiotherapy dose distribution; and generating the radiotherapy treatment plan based on the estimated realizable plan parameter.

CONVEX INVERSE PLANNING METHOD

The present invention relates to the field of radiation therapy. In particular, the present invention relates to methods for treatment planning for a radiation therapy system. The method includes setting a number of objectives reflecting clinical criteria are set for the regions of interest and generating radiation dose profiles to be delivered to these regions of interest. A convex optimization function for optimizing the delivered radiation based on the objectives is provided and dose profiles for specific treatment configurations including beam shape settings for the radiation dose profiles are calculated using the convex optimization function. Treatment plans including determining the radiation dose profiles to be delivered during treatment based on the treatment configurations are created and an optimal treatment plan that satisfies the clinical criteria is selected. 1. A method for treatment planning for a radiation therapy system , the radiation therapy system comprising a radiation therapy unit , wherein a spatial dose delivered can be changed by adjusting beam shape settings , said method comprising the steps of:setting a number of objectives reflecting clinical criteria for regions of interest, including targets to be treated during treatment of the patient, organs at risk and/or healthy tissue;modelling radiation dose profiles to be delivered to said target;providing a convex optimization problem that steers the delivered radiation according to the objectives;calculating dose profiles for specific treatment configurations including beam shape settings for the radiation dose profiles using said convex optimization problem;creating treatment plans including determining the radiation dose profiles to be delivered during treatment based on the treatment configurations, wherein each radiation dose profile is modelled by a spatial dose volume distribution of radiation, the shape of said spatial distribution depending on the beam shape settings; andselecting an ...

SYSTEM AND METHOD FOR AUTOMATIC TREATMENT PLANNING

The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan. 123.-. (canceled)24. A radiotherapy system for treating a target patient according to a treatment plan , the system configured to use training data associated with past treatment plans used to treat sample patients , the training data including a plurality of observations associated with conditions of the sample patients and derived from medical image data , the training data further including one or more plan outcomes reflecting outcomes resulting from the past treatment plans , or plan parameters reflecting design parameters of the past treatment plans , the system comprising: determining a joint probability density indicating a likelihood that both at least one particular observation and at least one particular plan outcome or plan parameter are present in the training data;', 'calculating a conditional probability based upon the determined joint probability density, wherein the conditional probability indicates a likelihood that the particular plan outcome or plan parameter is present in the training data;', ' ...

CONVEX INVERSE PLANNING METHOD

A method for treatment planning for a radiation therapy system includes setting a number of objectives reflecting clinical criteria are set for the regions of interest and generating radiation dose profiles to be delivered to these regions of interest. A convex optimization function for optimizing the delivered radiation based on the objectives is provided and dose profiles for specific treatment configurations including beam shape settings for the radiation dose profiles are calculated using the convex optimization function. Treatment plans including determining the radiation dose profiles to be delivered during treatment based on the treatment configurations are created and an optimal treatment plan that satisfies the clinical criteria is selected. 1. A treatment planning computer structure comprisinga calculation module configured for generating radiation dose profiles to be delivered to the target, for providing a convex optimization problem that steers the delivered radiation according to clinical criteria objectives for optimizing the delivered radiation based on the clinical criteria objectives, and for calculating dose profiles for specific treatment configurations including beam shape settings for the radiation dose profiles are calculated using the convex optimization function;a treatment plan module is configured for creating treatment plans including determining the radiation dose profiles to be delivered during treatment based on the treatment configurations, wherein each radiation dose profile is modelled by a spatial dose volume distribution of radiation, the shape of the spatial distribution depending on the beam shape settings; andan optimizing module is configured for selecting an optimal treatment plan that satisfies the clinical criteria.2. A treatment planning computer structure in a radiation therapy system , the radiation therapy system comprising a radiation therapy unit having a fixed radiation focus point , wherein a spatial dose ...

Modality-agnostic method for medical image representation

Techniques for the operation and use of a model that learns the general representation of multimodal images is disclosed. In various examples, methods from representation learning are used to find a common basis for representation of medical images. These include aspects of encoding, fusion, and downstream tasks, with use of the general representation and model. In an example, a method for generating a modality-agnostic model includes receiving imaging data, encoding the imaging data by mapping data to a latent representation, fusing the encoded data to conserve latent variables corresponding to the latent representation, and training a model using the latent representation. In an example, a method for processing imaging data using a trained modality-agnostic model includes receiving imaging data, encoding the data to the defined encoding, processing the encoded data with a trained model, and performing imaging processing operations based on output of the trained model.

SYSTEM AND METHOD FOR AUTOMATIC TREATMENT PLANNING

The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan. 1. A radiotherapy system , comprising:a memory storing computer executable instructions; and receiving a plurality of training data;', 'determining one or more predictive models based on the training data;', 'receiving patient specific testing data;', 'predicting a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data; and', 'generating a new treatment plan based on the prediction,', 'wherein the one or more predictive models are determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density., 'a processor device communicatively coupled to the memory, wherein the processor device is configured to execute the computer executable instructions for2. The radiotherapy system of claim 1 , wherein the processor device is further configured to execute the computer executable instructions for validating a previous ...

Modality-agnostic method for medical image representation

Techniques for the operation and use of a model that learns the general representation of multimodal images are disclosed. In various examples, methods from representation learning are used to find a common basis for representation of medical images. These include aspects of encoding, fusion, and downstream tasks, with use of the general representation and model. In an example, a method for generating a modality-agnostic model includes receiving imaging data, encoding the imaging data by mapping data to a latent representation, fusing the encoded data to conserve latent variables corresponding to the latent representation, and training a model using the latent representation. In an example, a method for processing imaging data using a trained modality-agnostic model includes receiving imaging data, encoding the data to the defined encoding, processing the encoded data with a trained model, and performing imaging processing operations based on output of the trained model.

Generation of realizable radiotherapy plans

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving an input parameter related to a patient, the input parameter being of a given type; processing the input parameter with a machine learning technique to estimate a realizable plan parameter of a radiotherapy treatment plan, wherein the machine learning technique is trained to establish a relationship between the given type of input parameter and a set of realizable radiotherapy treatment plan parameters to achieve a target radiotherapy dose distribution; and generating the radiotherapy treatment plan based on the estimated realizable plan parameter.

Radiotherapy treatment plans using differentiable dose functions

Techniques for generating a radiotherapy treatment plan parameter are provided. The techniques include receiving radiotherapy treatment plan information; processing the radiotherapy treatment plan information to estimate one or more radiotherapy treatment plan parameters based on a process that depends on the output of a subprocess that estimates a derivative of a dose calculation; and generating a radiotherapy treatment plan using the estimated one or more radiotherapy treatment plan parameters.

Systems and methods for optimizing treatment planning

The present disclosure relates to systems, methods, and computer-readable storage devices for radiotherapy treatment planning. For example, a method may generate a treatment plan for a patient. The method may receive training data reflecting radiotherapy treatment data. The training data may include a feature vector and a target vector. The method may further determine a training model based on the feature vector and the target vector. The method may further receive testing data associated with the patient. The testing data may include a descriptive feature vector. The method may further determine a therapy model based on the descriptive feature vector and the training model. The therapy model may be used to generate the treatment plan.

System and method for automatic treatment planning

The present disclosure relates to systems, methods, and computer-readable storage media for radiotherapy. Embodiments of the present disclosure may receive a plurality of training data and determine one or more predictive models based on the training data. The one or more predictive models may be determined based on at least one of a conditional probability density associated with a selected output characteristic given one or more selected input variables or a joint probability density. Embodiments of the present disclosure may also receive patient specific testing data. In addition, embodiments of the present disclosure may predict a probability density associated with a characteristic output based on the one or more predictive models and the patient specific testing data. Moreover, embodiments of the present disclosure may generate a new treatment plan based on the prediction and may use the new treatment plan to validate a previous treatment plan.

Radiotherapy planning systems

A treatment planning system for a radiotherapy apparatus comprises a processor that receives an input data set comprising: a dose distribution within a volume to be irradiated that is to be achieved by the radiotherapy apparatus; a three dimensional volume image that characterises tissue types within the volume; and a set of process parameters which characterise a delivery process of radiotherapy. The system outputs a treatment plan to deliver the dose distribution when implemented by the delivery process, by optimising a function that includes at least one part representing a time-dependent response of the tissue types to an applied radiation dose. The input data set may further comprise a number of treatment fractions over which the dose distribution is to be delivered and the processor arranged to assign fractional parts of the dose distribution to successive treatment fractions, taking into account time and dose dependent characteristics of the tissue types in the volume image. The three-dimensional volume image may characterise the tissue types according to at least their local microenvironment. The at least one part representing a time-dependent response of the tissue types may represent at least one of a cell repair model and a tumour diffusion model.

Parameter search in radiotherapy treatment plan optimization

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.

Computing radiotherapy dose distribution

Systems and methods for calculating radiotherapy dose distribution are provided. The systems and methods include operations for receiving data representing at least one of particle trajectories or a dose deposition pattern in a simulated delivery of a radiotherapy plan; applying a dose calculation process to the received data to generate a first radiotherapy dose distribution having a first level of detail; and processing the first radiotherapy dose distribution using a trained machine learning technique to generate a second radiotherapy dose distribution having a second level of detail that enhances the first level of detail.

Radiotherapy plan parameters with privacy guarantees

Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

Parameter search in radiotherapy treatment plan optimization

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.

Methods for adaptive radiotherapy

The present invention relates to the field of radiation therapy and methods, software and systems for adaptive radiotherapy. There is provided a method for adaptive radiotherapy treatment of a patient comprising receiving a sequence of measurement data of the treatment, the measurements being captured at different time points, mapping measurement data to a representation of a treatment geometry at the time points, using the representation in a dynamical model describing how variables in the representation evolve over time, based on the dynamical model, estimating positions over time of the treatment geometry or selected parts of the treatment geometry, determining a treatment action for the patient at a defined time point using the estimation from the dynamical model and executing the treatment action at the defined time point.

Radiotherapy plan parameters with privacy guarantees

Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

Treatment planning

The present disclosure relates to the field of radiation therapy and methods, software and systems for treatment planning.

Radiotherapy plan parameters with privacy guarantees

Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

Radiotherapy plan parameters with privacy guarantees

Techniques for producing segmentation with privacy are provided. The techniques include receiving a medical image; processing the medical image with a student machine learning model to estimate radiotherapy plan parameters, the student machine learning model being trained to establish a relationship between a plurality of public training medical images and corresponding radiotherapy plan parameters, the radiotherapy plan parameters of the plurality of public training medical images being generated by aggregating a plurality of radiotherapy plan parameter estimates produced by: processing the plurality of public training medical images with a plurality of teacher machine learning models to generate sets of radiotherapy plan parameter estimates; and reducing respective dimensions of the sets of radiotherapy plan parameter estimates or medical images, the radiotherapy plan parameters of the plurality of public training medical images being perturbed in accordance with privacy criteria; and generating a radiotherapy treatment plan based on the estimated radiotherapy plan parameters.

Generative model of phase space

Systems and methods are disclosed for simulating dose deposition. The systems and methods perform operations comprising: receiving a set of training data representing phase space of a radiotherapy treatment device comprising propagation and scattering of particles inside the radiotherapy treatment device; training a generative machine learning model based on the set of training data to generate one or more samples of the phase space of the radiotherapy treatment device; and simulating dose deposition at a particular region of interest based on the one or more samples of the phase space generated by the generative machine learning model.

Parallel generation of pareto optimal radiotherapy plans

Systems and methods are disclosed for dynamic adaptation of radiotherapy treatments. Example operations for radiotherapy treatment planning include: obtaining a radiotherapy problem (e.g., a multicriteria optimization problem) for providing radiotherapy treatment; performing treatment planning optimization for delivery of the radiotherapy treatment; and generating treatment plan data based on at least one of a plurality of solutions, with such treatment plan data being used to generate a radiation therapy treatment plan for a particular human subject with a radiotherapy machine. In an example, the treatment plan optimization includes identifying parameterized linear programming equations from the radiotherapy problem; converting the parameterized linear programming equations for execution by parallel processing hardware; and solving a plurality of the converted parameterized linear programming equations in parallel on the parallel processing hardware, to produce the plurality of solutions (e.g., encompassing the pareto surface) to the radiotherapy problem that correspond to the parameters that define the radiotherapy problem.

Continuum radiotherapy treatment planning

Systems and methods are disclosed for dynamic radiotherapy treatment planning in a continuous space of computation. Example operations for generating treatment plan data for a radiotherapy treatment include: obtaining data for a radiotherapy treatment of a human subject; generating a set of radiation controls from the data for the radiotherapy treatment, with at least one of the radiation controls being based on a mapping from a continuous (e.g., infinite dimensional) computational space; converting the generated set of radiation controls to a set of treatment delivery parameters, the set of treatment delivery parameters corresponding to capabilities of a radiotherapy treatment machine; and producing treatment plan data for the radiotherapy treatment based on the set of treatment delivery parameters.

Treatment planning

The present disclosure relates to the field of radiation therapy and methods, software and systems for treatment planning.

Radiotherapy plan parameters with privacy guarantees