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.