Reconstructing High-Quality Sodium MR Images from Limited Noisy k-Space Data with Model-Assisted Deep Learning
Yibo Zhao1,2, Yudu Li1,2, Rong Guo1,2, Keith R. Thulborn3, and Zhi-Pei Liang1,2
1Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, Urbana, IL, United States, 2Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, Urbana, IL, United States, 3Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States
Sodium MRI can acquire important biological information about cell integrity and tissue viability, but its clinical application has been limited by low SNR and poor spatial resolution. We propose a novel method to reconstruct high-quality sodium images from limited and noisy k-space data. The new method synergistically integrates model-based reconstruction with deep learning. Simulation and experimental results show that the proposed method can reconstruct high-SNR and high-resolution sodium images, which clearly delineate lesions such as brain tumors.
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