Systems and methods for magnetic resonance imaging standardization using deep learning
AssigneeSubtle Medical, Inc.
InventorsTao Zhang, Enhao Gong, Gregory Zaharchuk
Life of this patent
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Abstract
A computer-implemented method for transforming magnetic resonance (MR) imaging across multiple vendors is provided. The method comprises: obtaining a training dataset, wherein the training dataset comprises a paired dataset and an un-paired dataset, and wherein the training dataset comprises image data acquired using two or more MR imaging devices; training a deep network model using the training dataset; obtaining an input MR image; and transforming the input MR image to a target image style using the deep network model.
Claims20
A computer-implemented method for transforming magnetic resonance (MR) imaging across multiple vendors comprising:
- (a) obtaining a training dataset, wherein the training dataset comprises a paired dataset and an un-paired dataset, and wherein the training dataset comprises image data acquired using two or more MR imaging devices;
- (b) training a deep network model using the training dataset;
- (c) obtaining an input MR image;
- and
- (d) transforming the input MR image to a target image style using the deep network model.
A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
- (a) obtaining a training dataset, wherein the training dataset comprises a paired dataset and an un-paired dataset, and wherein the training dataset comprises image data acquired using two or more MR imaging devices;
- (b) training a deep network model using the training dataset;
- (c) obtaining an input MR image;
- and
- (d) transforming the input MR image to a target image style using the deep network model.
Dependencies are inferred from the claim text.