US 11,961,209 B2
Noise to noise ensemble learning for pet and CT image and data denoising
Chung Chan, Vernon Hills, IL (US); Jian Zhou, Vernon Hills, IL (US); and Evren Asma, Vernon Hills, IL (US)
Assigned to CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed by CANON MEDICAL SYSTEMS CORPORATION, Otawara (JP)
Filed on Sep. 4, 2020, as Appl. No. 17/013,104.
Claims priority of provisional application 62/923,593, filed on Oct. 20, 2019.
Prior Publication US 2021/0118098 A1, Apr. 22, 2021
Int. Cl. G06T 5/00 (2006.01); G06N 3/08 (2023.01)
CPC G06T 5/002 (2013.01) [G06N 3/08 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method of generating an image denoising system, comprising:
obtaining imaging data from a set of N studies;
dividing each of the N studies into at least K training-ready noise realizations representing K subsets of the imaging data from each of the N studies; and
training a machine learning-based system, on a study-by study-basis for each study of the set of N studies by a noise-to-noise-ensemble (N2NEN) training method, based on (1) a first noise realization of the at least K training-ready noise realizations only as training data for each study, and (2) a remaining K−1 training-ready noise realizations of the at least K training-ready noise realizations other than the first noise realization for each study only as label data to produce a trained machine learning-based system,
wherein the first noise realization and the remaining K−1 training-ready noise realizations contain noise.