Abstract #3447

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

Yuxin Hu^1,2, Qiyuan Tian^1,2, Yunyingying Xu², Philip K. Lee^1,2, Bruce L. Daniel^1,3, and Brian A. Hargreaves^1,2,3

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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