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 |
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.
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