US 12,482,149 B2
Under-sampled magnetic resonance image reconstruction of an anatomical structure based on a machine-learned image reconstruction model
Shanhui Sun, Lexington, MA (US); Zhang Chen, Brookline, MA (US); Xiao Chen, Lexington, 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 Apr. 3, 2023, as Appl. No. 18/130,150.
Prior Publication US 2024/0331222 A1, Oct. 3, 2024
Int. Cl. G06T 11/00 (2006.01)
CPC G06T 11/005 (2013.01) [G06T 2210/41 (2013.01); G06T 2211/424 (2013.01)] 16 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
at least one processor configured to:
obtain an under-sampled magnetic resonance (MR) image of an anatomical structure; and
reconstruct the under-sampled MR image of the anatomical structure through multiple iterations based on a machine-learned (ML) image reconstruction model, wherein the ML image reconstruction model is learned through a training process and during the training process:
the ML image reconstruction model is used to predict a correction to an input MR image obtained during at least one of the multiple iterations and generate a reconstructed MR image by applying the correction to the input MR image;
an ML reward model is used to determine a reward for the reconstructed MR image generated using the ML image reconstruction model; and
parameters of the ML image reconstruction model are adjusted based on the reward determined by the ML reward model, wherein, prior to being used in the training process of the ML image reconstruction model, the ML reward model is pre-trained for predicting a quality of an MR image and generating an evaluation for the MR image based on the predicted quality, the pre-training of the ML reward model is conducted based at least on a first MR training image, a second MR training image, and an indication that the second MR training image has a higher quality than the first MR training image, and the ML reward model is used during the pre-training to extract respective features from the first MR training image and the second MR training image and predict a quality of the first MR training image based on a difference between the respective features extracted from the first MR training image and the second MR training image.