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Abstract #4314

Compressed SVD for L+S Matrix Decomposition Model to Reconstruct Undersampled Dynamic MRI

Muhammad Shafique1,2, Sohaib Ayyaz Qazi1,3, Irfan Ullah1, and Hammad Omer1
1Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan, 2Electrical Engineering, University of the Poonch Rawalakot, Rawalakot, Pakistan, 3Service of Radiology and Faculty of Medicine, University of Geneva University Switzerland, Geneva, Swaziland


The Low rank and Sparse (L+S) matrix decomposition model has been proposed in literature to reconstruct the undersampled dynamic MRI data. The limitations of L+S method include an effective separation of the low-rank and sparse components from the acquired dynamic MRI data; also the algorithm is computationally expensive. In this paper, Compressed Singular Value Decomposition (cSVD) is employed in L+S method. The results show that the proposed method provides effective separation of the L and S components as well as considerably reduces the computation time.

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