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Proceedings Paper

Sparse decomposition learning based dynamic MRI reconstruction
Author(s): Peifei Zhu; Qieshi Zhang; Sei-ichiro Kamata
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Paper Abstract

Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15~20% of Mean Square Error (MSE) compared to other existing methods.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944516 (14 February 2015); doi: 10.1117/12.2180534
Show Author Affiliations
Peifei Zhu, Waseda Univ. (Japan)
Qieshi Zhang, Waseda Univ. (Japan)
Sei-ichiro Kamata, Waseda Univ. (Japan)

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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