US 11,756,197 B2
Systems and methods of processing magnetic resonance images using phase-sensitive structural similarity index measure
Sangtae Ahn, Guilderland, NY (US); Uri Wollner, Ramat Gan (IL); Graeme C. Mckinnon, Hartland, WI (US); Rafael Shmuel Brada, Hod-Hasharon (IL); and Christopher Judson Hardy, Schenectady, NY (US)
Assigned to GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US)
Filed by GE PRECISION HEALTHCARE LLC, Wauwatosa, WI (US)
Filed on Mar. 10, 2021, as Appl. No. 17/197,854.
Prior Publication US 2022/0292679 A1, Sep. 15, 2022
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06V 10/60 (2022.01)
CPC G06T 7/0014 (2013.01) [G06N 3/08 (2013.01); G06V 10/60 (2022.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30016 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of processing complex magnetic resonance (MR) images, comprising:
receiving a pair of corrupted complex data and pristine complex images corresponding to the corrupted complex data; and
training a neural network model using the pair of corrupted complex data and pristine complex images by:
inputting the corrupted complex data to the neural network model;
setting the pristine complex images as target outputs of the neural network model;
processing the corrupted complex data using the neural network model to derive output complex images of the corrupted complex data;
comparing the output complex images with the target outputs by computing a phase-sensitive structural similarity index measure (PS-SSIM) between each of the output complex images and its corresponding target complex image, wherein the PS-SSIM is real-valued and varies with phases of the output complex image and phases of the target complex image; and
adjusting the neural network model based on the comparison.