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.