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Abstract #4969

Knee Imaging at 0.05 T with Deep Learning Superresolution Reconstruction

Ye Ding1,2, Vick Lau1,2, Shi Su1,2, Xiang Li1,2, Jiahao Hu1,2, Junhao Zhang1,2, Xuehong Lin1,2, Liubin Wu1,2, Aijing Lin1,2, Alex T.L. Leong1,2, Yujiao Zhao1,2, and Ed X. Wu1,2
1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong, 2Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, Hong Kong

Synopsis

Keywords: Bone/Skeletal, Low-Field MRI

Motivation: There has been significant development in ultralow-field (ULF) MRI for low-cost, shielding-free, and point-of-care extremity applications. However, its image quality remains poor, and scan times are long.

Goal(s): We aim to advance the speed and quality of knee ULF MRI using 2D partial Fourier sampling and deep learning image formation.

Approach: A fast acquisition and deep learning reconstruction framework to accelerate knee MRI at 0.05 tesla was proposed.

Results: 3D deep learning leverages high-field knee anatomy data to enhance image quality, reduce artifacts and noise, and improve spatial resolution.

Impact: The method effectively overcomes the low-signal barrier, reconstructing fine anatomical structures at 0.05 Tesla that are reproducible within subjects and consistent across two protocols. It enables rapid, high-quality ULF MRI for potential point-of-care applications.

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Keywords