@inproceedings{7b87bd047a2845fb8eef2429a6c4501d,
title = "Tree structure based MR image reconstruction with partially known support",
abstract = "As a promising sampling scheme, compressed sensing (CS) has been successfully used for magnetic resonance imaging (MRI). By exploiting the sparse property, MR images can be reconstructed from undersampled k-space data. However, images involved in practical applications display structural information in addition to the sparsity. In this paper, we simultaneously take advantage of the wavelet tree structure and the support information, thereby proposing a new MR image reconstruction method. The resulting reconstruction model is composed of a data fidelity term, a total variation (TV) regularization term and a mixed l2-l1 norm term penalizing the parent-child pairs within the complement of the known support. The proposed method has been validated by experiments both on synthetic and practical MRI data. The results demonstrate the competitive performance of our method over the conventional CS reconstruction method.",
keywords = "Image reconstruction, Magnetic resonance imaging, Support information, Union-of-subspaces, Wavelet tree structure",
author = "Yu Han and Xiangzhen Gao and Huiqian Du and Yize Dong and Wenbo Mei",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 12th IEEE International Conference on Signal Processing, ICSP 2014 ; Conference date: 19-10-2014 Through 23-10-2014",
year = "2014",
doi = "10.1109/ICOSP.2014.7015304",
language = "English",
series = "International Conference on Signal Processing Proceedings, ICSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "October",
pages = "1801--1804",
editor = "Baozong Yuan and Qiuqi Ruan and Xiaofang Tang",
booktitle = "ICSP 2014 - 2014 IEEE 12th International Conference on Signal Processing, Proceedings",
address = "United States",
edition = "October",
}