A Non-Local Rician Noise Reduction Approach for 3-D Magnitude Magnetic Resonance Images

The visual quality of Magnetic Resonance Images (MRI) plays an important role in accuracy of clinical diagnosis which can be seriously degraded by existing noise during acquisition process. Therefore, denoising is of great interest for diagnostic aims and also the ability of automatic computerized analysis. Noise in Magnitude MRI is usually modeled by Rician distribution which introduces a signal-dependent bias and reduces the image contrast. In this article an efficient approach for enhancement of the noisy magnitude MRI based on the recently proposed linear minimum mean square error (LMMSE) estimator is introduced. The natural redundancy of the acquired MR data is employed to improve the performance of unknown signal estimation. Since in practice, the MR data is in a large majority 3-D, the proposed method is developed to deal with 3-D MR volumes. The quantitative and qualitative metrics have been used to demonstrate and compare the performance of the introduced approach with several state-of-arts denoising schemes. Experimental results show that the proposed method restores delicate structural details conveniently while the computational cost remains low.