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

Jun Miao¹, Wen Li¹, Sreenath Narayan¹, Xin Yu¹, David L. Wilson^1,2

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.