Improved k - t PCA algorithm using artificial sparsity in dynamic MRI

Wang, Yiran, Chen, Zhifeng, Wang, Jing, Yuan, Lixia, Xia, Ling and Liu, Feng (2017) Improved k - t PCA algorithm using artificial sparsity in dynamic MRI. Computational and Mathematical Methods in Medicine, 2017 4816024. doi:10.1155/2017/4816024


Author Wang, Yiran
Chen, Zhifeng
Wang, Jing
Yuan, Lixia
Xia, Ling
Liu, Feng
Title Improved k - t PCA algorithm using artificial sparsity in dynamic MRI
Journal name Computational and Mathematical Methods in Medicine   Check publisher's open access policy
ISSN 1748-6718
1748-670X
Publication date 2017-01-01
Sub-type Article (original research)
DOI 10.1155/2017/4816024
Open Access Status DOI
Volume 2017
Start page 4816024
Total pages 13
Place of publication New York, NY, United States
Publisher Hindawi
Language eng
Subject 2700 Medicine
2611 Modelling and Simulation
2400 Immunology and Microbiology
1300 Biochemistry, Genetics and Molecular Biology
2604 Applied Mathematics
Abstract The k-t principal component analysis (k-t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k-t PCA that combines the k-t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k-t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k-t space from the original reconstructed k-t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k-t PCA algorithm, the sparse k-t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 61671405
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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