@inproceedings{25f1403cd2c54b28b16a481366064518,
title = "Dynamic MRI Reconstruction Using Tensor-SVD",
abstract = "In this paper we propose to reconstruct dynamic magnetic resonance images from highly sparse sampling k-t space data by enhancing the low rankness and sparsity simultaneously. We introduce Tensor Singular Value Decomposition (t-SVD) instead of matrix SVD to maintain the structure of dynamic MR images. The reconstruction is casted into an optimization framework where the tensor nuclear norm (TNN) minimization is used to enhance the low rankness and the l1 norm minimization of tensor gradient along each mode is applied to enhance the sparsity. In addition, we utilize alternating direction method of multipliers (ADMM) algorithm to efficiently solve the proposed optimization problem. Experimental results demonstrate the superior performance of the proposed method.",
keywords = "Dynamic Magnetic Resonance Imaging (dMRI), Low rank Tensor, Tensor-SVD (t-SVD)",
author = "Jianhang Ai and Shuli Ma and Huiqian Du and Liping Fang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th IEEE International Conference on Signal Processing, ICSP 2018 ; Conference date: 12-08-2018 Through 16-08-2018",
year = "2019",
month = feb,
day = "2",
doi = "10.1109/ICSP.2018.8652421",
language = "English",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1114--1118",
editor = "Yuan Baozong and Ruan Qiuqi and Zhao Yao and An Gaoyun",
booktitle = "2018 14th IEEE International Conference on Signal Processing Proceedings, ICSP 2018",
address = "United States",
}