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Abstract #3447

Weighted-nuclear-norm minimization for low-rank approximation -- application to multi-shot diffusion-weighted MRI reconstruction

Yuxin Hu1,2, Qiyuan Tian1,2, Yunyingying Xu2, Philip K. Lee1,2, Bruce L. Daniel1,3, and Brian A. Hargreaves1,2,3
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3Department of Bioengineering, Stanford University, Stanford, CA, United States

Low rankness of image/k-space data has been exploited in many different MRI applications. In this work, we introduce weighted-nuclear-norm minimization for MRI low-rank reconstruction, in which smaller thresholds are used for larger eigenvalues to reduce information loss. Our simulation results demonstrate that weighted nuclear norm could serve as a better rank approximation compared to (unweighted) nuclear norm. With this technique, we achieve 10-shot DWI and high-fidelity half-millimetre DWI reconstruction with significantly reduced ghosting artifacts.

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