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US 11,961,209 B2Granted
Noise to noise ensemble learning for pet and CT image and data denoising
AssigneeCanon Medical Systems Corporation
InventorsChung Chan, Jian Zhou, Evren Asma
Life of this patent
Estimating the term…
Abstract
A system and method for training a neural network to denoise images. One noise realization is paired to an ensemble of training-ready noise realizations, and fed into a neural network for training. Training datasets can also be retrospectively generated based on existing patient studies to increase the number of training datasets.
Claims18
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.
2 more elements
A system, comprising:
- processing circuitry configured to
- obtain imaging data from a set of N studies;
- divide 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
- train 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 the 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.
3 more elements
Dependencies are inferred from the claim text.