Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI
Joint Authors
Liu, Feng
Wang, Yiran
Chen, Zhifeng
Wang, Jing
Yuan, Lixia
Xia, Ling
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Yiran& Chen, Zhifeng& Wang, Jing& Yuan, Lixia& Xia, Ling& Liu, Feng. 2017. Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1142130
Modern Language Association (MLA)
Wang, Yiran…[et al.]. Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1142130
American Medical Association (AMA)
Wang, Yiran& Chen, Zhifeng& Wang, Jing& Yuan, Lixia& Xia, Ling& Liu, Feng. Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1142130
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142130