IMAGE SYNTHESIS USING ADVERSARIAL NETWORKS SUCH AS FOR RADIATION THERAPY
First Claim
1. A computer-implemented method for synthesizing a medical image using a trained statistical learning model, the method comprising:
- receiving medical imaging data obtained using a first imaging modality type;
applying the trained statistical learning model to the received medical imaging data to synthesize a medical image corresponding to a different second imaging modality type; and
providing the synthesized medical image for presentation or for use in further processing;
wherein the trained statistical learning model is established at least in part using a similarity determination between training imaging data provided at the model input, the training imaging data corresponding to the first imaging modality type and synthesized imaging data at the model output corresponding to the second imaging modality type; and
wherein the trained statistical learning model is established at least in part using a separate statistical learning model, the separate statistical learning model established to discriminate between actual imaging data corresponding to the second imaging modality and the synthesized imaging data.
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Accused Products
Abstract
A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.
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Citations
20 Claims
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1. A computer-implemented method for synthesizing a medical image using a trained statistical learning model, the method comprising:
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receiving medical imaging data obtained using a first imaging modality type; applying the trained statistical learning model to the received medical imaging data to synthesize a medical image corresponding to a different second imaging modality type; and providing the synthesized medical image for presentation or for use in further processing; wherein the trained statistical learning model is established at least in part using a similarity determination between training imaging data provided at the model input, the training imaging data corresponding to the first imaging modality type and synthesized imaging data at the model output corresponding to the second imaging modality type; and wherein the trained statistical learning model is established at least in part using a separate statistical learning model, the separate statistical learning model established to discriminate between actual imaging data corresponding to the second imaging modality and the synthesized imaging data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method for establishing a trained statistical learning model for synthesizing a medical image without requiring paired imaging for training, the method comprising:
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receiving training medical imaging data corresponding to a first imaging modality type; applying a statistical learning model to the received medical imaging data to synthesize imaging data corresponding to a different second imaging modality type; adjusting the statistical learning model at least in part using a similarity determination between the training imaging data and synthesized imaging data at the model output; and adjusting the statistical learning model at least in part using a separate statistical learning model, the separate statistical learning model established to discriminate between the synthesized imaging data and actual imaging data corresponding to the second imaging modality type. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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processing circuitry comprising at least one processor; and a storage medium comprising instructions, which when executed by the at least one processor, cause the processor to; receive medical imaging data obtained using a first imaging modality type; apply the trained statistical learning model to the received medical imaging data to synthesize a medical image corresponding to a different second imaging modality type; and provide the synthesized medical image for presentation or for use in further processing; wherein the trained statistical learning model is established at least in part using a similarity determination between training imaging data provided at the model input, the training imaging data corresponding to the first imaging modality type and synthesized imaging data at the model output corresponding to the second imaging modality type; and wherein the trained statistical learning model is established at least in part using a separate statistical learning model, the separate statistical learning model established to discriminate between the synthesized imaging data and actual imaging data corresponding to the second imaging modality type. - View Dependent Claims (20)
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Specification