US 12,406,412 B2
System and method for deep learning-based chemical shift artifact mitigation of non-Cartesian magnetic resonance imaging data
Sagar Mandava, Atlanta, GA (US); Robert Marc Lebel, Calgary (CA); Michael Carl, San Marcos, CA (US); and Florian Wiesinger, Freising (DE)
Assigned to GE Precision Healthcare LLC, Wauwatosa, WI (US)
Filed by GE Precision Healthcare LLC, Wauwatosa, WI (US)
Filed on Jan. 30, 2023, as Appl. No. 18/102,834.
Prior Publication US 2024/0257414 A1, Aug. 1, 2024
Int. Cl. G06N 3/08 (2023.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01); G06T 11/00 (2006.01)
CPC G06T 11/008 (2013.01) [G01R 33/4824 (2013.01); G01R 33/5608 (2013.01); G06N 3/08 (2013.01); G06T 2210/41 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a chemical shift artifact corrected reconstructed image from magnetic resonance imaging (MRI) data, comprising:
inputting into a trained deep neural network an image generated from the MRI data acquired during a non-Cartesian MRI scan of a subject;
utilizing the trained deep neural network to generate the chemical shift artifact corrected reconstructed image from the image, wherein the trained deep neural network was trained utilizing a tissue mixing model that models interactions between different tissue types to mitigate chemical shift artifacts, and wherein the tissue mixing model comprises a partial volume map for approximating a respective fraction of the different tissue types in each voxel of the image; and
outputting from the trained deep neural network the chemical shift artifact corrected reconstructed image.