US 12,437,187 B2
MRI reconstruction based on generative models
Zhang Chen, Cambridge, MA (US); Siyuan Dong, New Haven, CT (US); Shanhui Sun, Cambridge, MA (US); Xiao Chen, Cambridge, MA (US); Yikang Liu, Cambridge, MA (US); and Terrence Chen, Lexington, MA (US)
Assigned to Shanghai United Imaging Intelligence Co., Ltd., Shanghai (CN)
Filed by Shanghai United Imaging Intelligence Co., Ltd., Shanghai (CN)
Filed on Aug. 19, 2022, as Appl. No. 17/891,702.
Prior Publication US 2024/0062047 A1, Feb. 22, 2024
Int. Cl. G06N 3/047 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/047 (2023.01) [G06N 3/045 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
one or more processors configured to:
obtain a reconstructed magnetic resonance imaging (MRI) image of an anatomical structure, wherein the reconstructed MRI image is generated based on under-sampled MRI data associated with the anatomical structure and using a first artificial neural network trained for generating the reconstructed MRI image based on the under-sampled MRI data; and
process the reconstructed MRI image through a second artificial neural network, wherein the second artificial neural network comprises a generator network and a discriminator network, the generator network is used to refine the reconstructed MRI image of the anatomical structure, the discriminator network is used to supervise the generator network, and a refined MRI image of the anatomical structure is generated as a result of the processing, the refined MRI image characterized by an improved sharpness over the reconstructed MRI image.