US 11,935,231 B2
Contrast dose reduction for medical imaging using deep learning
Greg Zaharchuk, Stanford, CA (US); Enhao Gong, Sunnyvale, CA (US); and John M. Pauly, Stanford, CA (US)
Assigned to The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed by The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Filed on Apr. 26, 2021, as Appl. No. 17/239,898.
Application 17/239,898 is a continuation of application No. 16/155,581, filed on Oct. 9, 2018, granted, now 10,997,716.
Claims priority of provisional application 62/570,068, filed on Oct. 9, 2017.
Prior Publication US 2021/0241458 A1, Aug. 5, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06T 3/60 (2006.01); G06T 5/00 (2006.01); G06T 7/50 (2017.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06T 7/0012 (2013.01) [G06N 3/08 (2013.01); G06T 3/60 (2013.01); G06T 5/002 (2013.01); G06T 7/50 (2017.01); G16H 30/40 (2018.01); G06T 2207/10072 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10121 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30004 (2013.01); G16H 50/20 (2018.01)] 13 Claims
OG exemplary drawing
 
1. A method for improving quality of a medical image of a subject, the method comprising:
a) acquiring a first image with a first image acquisition sequence, wherein the first image is acquired with zero contrast agent dose administered to the subject;
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, wherein the second image is acquired with zero contrast agent dose administered to the subject;
c) processing the first image and the second image 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.