US 12,228,629 B2
Deep learning methods for noise suppression in medical imaging
Neel Dey, Brooklyn, NY (US); Jo Schlemper, Long Island City, NY (US); Seyed Sadegh Moshen Salehi, Bloomfield, NJ (US); Michal Sofka, Princeton, NJ (US); and Prantik Kundu, Branford, CT (US)
Assigned to Hyperfine Operations, Inc., Guilford, CT (US)
Filed by Hyperfine Operations, Inc., Guilford, CT (US)
Filed on Oct. 7, 2021, as Appl. No. 17/496,104.
Claims priority of provisional application 63/155,696, filed on Mar. 2, 2021.
Claims priority of provisional application 63/088,672, filed on Oct. 7, 2020.
Prior Publication US 2022/0107378 A1, Apr. 7, 2022
Int. Cl. G01R 33/56 (2006.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 5/70 (2024.01)
CPC G01R 33/56 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 5/70 (2024.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method of denoising magnetic resonance (MR) images, the method comprising:
using at least one computer hardware processor to perform:
obtaining a noisy MR image of a subject, the noisy MR image associated with a target domain;
denoising the noisy MR image of the subject using a denoising neural network model to obtain a denoised MR image, the denoising neural network model trained by:
generating first training data for training a first neural network model to denoise MR images at least in part by generating a first plurality of noisy MR images using: (1) clean MR data associated with a source domain, wherein the clean MR data associated with the source domain comprises MR data that is collected using a magnetic resonance imaging (MRI) system having a main magnetic field strength of 0.5 T or greater; and (2) first MR noise data associated with the target domain;
training the first neural network model using the first training data;
generating training data for training the denoising neural network model at least in part by applying the first neural network model to a second plurality of noisy MR images and generating a corresponding plurality of denoised MR images, wherein the second plurality of noisy MR images is generated using second noisy MR data associated with the target domain, and wherein the second noisy MR data associated with the target domain comprises MR data that is collected using an MRI system having a main magnetic field strength greater than or equal to 20 mT and less than or equal to 0.2 T; and
training the denoising neural network model using the training data for training the denoising neural network model; and
outputting the denoised MR image.