US 12,064,281 B2
Method and system for denoising CT images using a neural network
Jinyi Qi, Davis, CA (US); Nimu Yuan, Davis, CA (US); and Jian Zhou, Vernon Hills, IL (US)
Assigned to The Regents of the University of California, Oakland, CA (US); and CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed by The Regents of the University of California, Oakland, CA (US); and CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed on Jul. 24, 2020, as Appl. No. 16/938,463.
Claims priority of provisional application 62/991,269, filed on Mar. 18, 2020.
Prior Publication US 2021/0290191 A1, Sep. 23, 2021
Int. Cl. G06T 5/00 (2006.01); A61B 6/00 (2006.01); G06N 3/08 (2023.01)
CPC A61B 6/5211 (2013.01) [A61B 6/5258 (2013.01); A61B 6/542 (2013.01); G06N 3/08 (2013.01); G06T 5/002 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A method of generating an image denoising system, the method comprising:
acquiring single scan data obtained from a single scan of a subject, the single scan data being count-domain projection data;
identically distribute the acquired single scan data to generate first and second substantially independent partial scan data, wherein the first partial scan data is generated by applying a thinning model to the projection data and the second partial scan data is generated by subtracting the generated first partial scan data from the projection data; and
training a machine learning-based system based on the generated first substantially independent, identically distributed, partial scan data as input training data, and the generated second substantially independent, identically distributed, partial scan data as label data to produce a trained machine learning-based system.