@inproceedings{a4fc2a0ff0754af4a2b02b641bcea894,
title = "Motion compensated dynamic MRI reconstruction exploiting sparsity and low rank structure",
abstract = "In this paper, we propose a motion compensated dynamic magnetic resonance imaging (MRI) reconstruction method based on compressed sensing. First, a motion compensation method is used to improve the sparsity in temporal finite difference domain and the nuclear norm of the low rank property. Furthermore, the effective regularization terms are designed to enforce the low rank structure of dynamic scenes and sparsity in finite difference domain along spatial and temporal dimension simultaneously. To efficiently solve the proposed corresponding optimization problem, we decouple this problem into four sub-problems. Demons algorithm and Fast Composite Splitting Algorithm (FCSA), iterative shrinkage thresholding algorithm (ISTA) and conjugate gradient (CG) algorithm are employed to efficiently solve these sub-problems. The performance of the proposed method was evaluated on dynamic cardiac MRI dataset and experimental results demonstrate its effectiveness and robustness comparing with the current methods in CS dynamic MRI reconstruction.",
keywords = "compressed sensing (CS), dynamic magnetic resonance imaging (DMRI), finite difference, low rank, motion compensation",
author = "Ru Jia and Huiqian Du",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th IEEE International Conference on Signal Processing, ICSP 2016 ; Conference date: 06-11-2016 Through 10-11-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ICSP.2016.7877788",
language = "English",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--22",
editor = "Yuan Baozong and Ruan Qiuqi and Zhao Yao and An Gaoyun",
booktitle = "ICSP 2016 - 2016 IEEE 13th International Conference on Signal Processing, Proceedings",
address = "United States",
}