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Abstract #3822

Denoising in Parallel Imaging Via Structured Low-Rank Matrix Approximation

Derya Gol1, Lee C. Potter1

1Electrical & Computer Engineering and Davis Heart & Lung Institute, The Ohio State University, Columbus, OH, United States

Interpolation approaches in parallel MRI exhibit a noise amplification effect that may be mitigated via regularization techniques which are computationally expensive. In this study, we propose a pre-processing technique based on structured low-rank matrix approximation via truncated singular value decomposition (TSVD), which is able to suppress noise and ghost artifacts efficiently. TSVD method has been previously used in parallel MRI to improve the conditioning of the system matrix and to reconstruct k-space via matrix completion. In contrast to previous work, here rank properties are used to denoise acquired data in a computationally simple preprocessing for GRAPPA reconstruction.