Sparse decomposition learning based dynamic MRI reconstruction

Peifei Zhu; Qieshi Zhang; Sei-ichiro Kamata

doi:10.1117/12.2180534

14 February 2015 Sparse decomposition learning based dynamic MRI reconstruction

Peifei Zhu, Qieshi Zhang, Sei-ichiro Kamata

Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 944516 (2015) https://doi.org/10.1117/12.2180534
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy

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.

Citation Download Citation

Peifei Zhu, Qieshi Zhang, and Sei-ichiro Kamata "Sparse decomposition learning based dynamic MRI reconstruction", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944516 (14 February 2015); https://doi.org/10.1117/12.2180534

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KEYWORDS

Associative arrays

Magnetic resonance imaging

Reconstruction algorithms

Compressed sensing

Data acquisition

Evolutionary algorithms

Data modeling

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