Abstract #1176

Reconstruction of Undersampled Dynamic MRI Data Using Truncated Nuclear Norm Minimization and Sparsity Constraints

Runyu Yang¹, Yuze Li¹, and Huijun Chen¹

¹Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

Achieving high spatio-temporal resolutions is challenging in dynamic magnetic resonance imaging (dMRI). It is effective to use low-rank and sparse jointly for dMRI reconstruction. However, the nuclear norm is usually used as a convex approximation of rank function in many low-rank models, so the result is not optimal. In this study, we proposed a novel method used low rank which utilize a nonconvex norm and sparse jointly for dMRI reconstruction. The effectiveness of the proposed method was investigated in phantom and in-vivo experiments.

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