Abstract #4693

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

Lihong Tang¹, Yibo Zhao^2,3, Yudu Li^2,3, Rong Guo^2,3, Bingyang Cai¹, Yao Li¹, Zhi-Pei Liang^2,3, 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

Synopsis

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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