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Abstract #4890

MR Rician Noise Reduction in Diffusion Tensor Imaging Using Compressed Sensing by Sampling Decomposition

Jun Miao1, Wen Li1, Sreenath Narayan1, Xin Yu1, David L. Wilson1,2

1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; 2Radiology, University Hospitals of Cleveland

Reduction of Rician noise in MRI is very much desired, particularly in low signal-to-noise ratio (SNR) images such as diffusion tensor imaging. We used compressed sensing to reduce noise by decomposing full k-space data into multiple sets of incoherent subsamples, reconstructing full k-space individually, and aggregate them to be the final k-space data. Noise can be significantly suppressed in image and fractional anisotropy (FA) estimation can be significantly improved.