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Abstract #3095

Super-resolution reconstruction of Knee MRI at 3T and 7T based on the Hybrid Attention Transformer

Pinzhen Chen1, Libo Xu2, Boyang Pan2, Jing Li1, Xi Yang1, Yuting Wang1, Wei Chen3, Long Qian4, Nan-jie Gong5, and Wei Chen1
17T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China., Chong Qing, China, 2RadioDynamic Medical, Shanghai, China., Shang Hai, China, 3MR Research Collaboration Team, Siemens Healthineers Ltd., Wuhan, China, Wuhan, China, 4MR Research Collaboration Team, Siemens Healthineers Ltd., Guangzhou, China, Guangzhou, China, 5Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Tsinghua University, Beijing China, Bei Jing, China

Synopsis

Keywords: Joint, AI/ML Image Reconstruction

Motivation: Enhancing lower-resolution images from 3T/7T MR to match high-resolution images of 7T using HAT algorithm could combine the diagnostic benefits of 7T for knee disorders with the availability of 3T.

Goal(s): To investigate the performance of HAT algorithm in super-resolution reconstruction for improving lower-resolution images from 3T and 7T MR.

Approach: For the 7T knee image task, we used MSE-based HAT with 0.8mm and 0.4mm datasets, then fine-tuned a GAN-based 3T restoration model on 3T-7T pairs using the pre-trained 7T SR model.

Results: The MRI-based knee HAT algorithm enhanced the image quality and spatial resolution of lower-resolution imaging from 3T and 7T MR.

Impact: This study demonstrates the potential of accurately identifying and characterizing peripheral nerve pathology in the extremities utilizing 3D DESS and PD-TSE FS sequences at 7T MR.

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Keywords