Dynamic Magnetic Resonance Imaging via Nonconvex Low-Rank Matrix Approximation

Dynamic Magnetic Resonance Imaging via Nonconvex Low-Rank Matrix Approximation

Reconstruction of highly accelerated dynamic magnetic resonance imaging (MRI) is of crucial importance for the medical diagnosis. The application of general robust principal component analysis (RPCA) to MRI can increase imaging speed and efficiency. However, conventional RPCA makes use of nuclear no...

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Bibliographic Details
Main Authors: Fei Xu, Jingqi Han, Yongli Wang, Ming Chen, Yongyong Chen, Guoping He, Yunhong Hu
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Magnetic resonance imaging
nonconvex
low-rank matrix approximation
Online Access:https://ieeexplore.ieee.org/document/7831389/

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