Contrast Dose Reduction for Medical Imaging Using Deep Learning
AssigneeThe Board of Trustees of the Leland Stanford Junior University
InventorsGreg Zaharchuk, Enhao Gong, John M. Pauly
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Abstract
A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [114] that has been trained using zero-contrast [100] and low-contrast [102] images as input to the DLN and full-contrast images [104] as reference ground truth images. Prior to training, the images are pre-processed [106, 110, 118] to co-register and normalize them. The trained DLN [114] is then used to predict a synthesized full-dose contrast agent image [116] from acquired zero-dose and low-dose images.
Claims13
A method for performing medical diagnostic imaging of a subject with reduced contrast agent dose, the method comprising:
- a) acquiring a first image with a first image acquisition sequence;
- b) acquiring a second image with a second image acquisition sequence distinct from the first image acquisition sequence, wherein the first image and the second image are acquired using a common imaging modality and with a zero contrast agent dose or low contrast agent dose administered to the subject, where the low contrast agent dose is less than a full contrast agent dose;
- c) processing the first image and the second image to co-register and normalize the images to adjust for acquisition and scaling differences;
- and
- d) applying the first image and the second image as input to a deep learning network (DLN) to generate as output of the DLN an image of the subject with an enhanced quality.
Dependencies are inferred from the claim text.