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Abstract #4693

Joint Sensitivity Estimation and Image Reconstruction in Parallel Imaging Using Pre-learned Subspaces of Coil Sensitivity Functions

Lihong Tang1, Yibo Zhao2,3, Yudu Li2,3, Rong Guo2,3, Bingyang Cai1, Yao Li1, Zhi-Pei Liang2,3, and Jie Luo1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, United States, 3Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, Urbana, IL, United States


Accurate estimation of coil sensitivity functions is essential for SENSE-based image reconstruction. This paper presents a new method for joint estimation of coil sensitivity functions and image reconstruction from sparsely sampled k-space data without any auto-calibration data. The proposed method uses a probabilistic subspace model to capture statistical spatial distributions of a given coil system from prior scan data. The proposed method has been validated using experimental data (fastMRI dataset), producing high-quality reconstruction results from calibrationless sparse data (acceleration factor R = 8 or R = 16). The proposed method may further enhance the practical utility of parallel imaging.

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