29-09-2022 дата публикации
Номер: US20220308242A1
A method for x-ray photon-counting data correction. The method includes generating, by a training data generation module, training input spectral projection data based, at least in part, on a reference spectral projection data. The training input spectral projection data includes at least one of a pulse pileup distortion, a charge splitting distortion, and/or noise. The method further includes training, by a training module, a data correction artificial neural network (ANN) based, at least in part, on training data. The data correction ANN includes a pulse pileup correction ANN, and a charge splitting correction ANN. The training data includes the training input spectral projection data and the reference spectral projection data. 1. A method for x-ray photon-counting data correction , the method comprising:generating, by a training data generation module, training input spectral projection data based, at least in part, on a reference spectral projection data, the training input spectral projection data comprising at least one of a pulse pileup distortion, a charge splitting distortion, and/or noise; andtraining, by a training module, a data correction artificial neural network (ANN) based, at least in part, on training data, the data correction ANN comprising a pulse pileup correction ANN, and a charge splitting correction ANN, the training data comprising the training input spectral projection data and the reference spectral projection data.2. The method of claim 1 , wherein the training is performed in a Wasserstein generative adversarial network (WGAN) framework.3. The method of claim 1 , further comprising:generating, by the pulse pileup correction ANN, an intermediate estimate based, at least in part, on the training input spectral projection data; anddetermining, by a guidance loss circuitry, a guidance loss based, at least in part, on the intermediate estimate, and based, at least in part, on a charge splitting distorted target, the charge splitting distorted ...
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