Abstract #4314

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

Muhammad Shafique^1,2, Sohaib Ayyaz Qazi^1,3, 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

Synopsis

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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