A Robust Noise Reduction Strategy in Magnetic Resonance Images

Abstract

Magnetic Resonance imaging (MRI)is an indispensable tool and plays an important role in diagnosing the tumors in soft tissue. However, the added noise to the MRI scan during the acquisition will degrade the quality, which will result in fault/incorrect diagnosis of the disease.In order to address this challenge, time averaging concept was introduce to increase the signal noise ratio, but this concept will decrease the spatial resolution and increase the acquisition time, which in turnwill increase the patient exposure time to the radiation. Then the ray of hope was on computational methods and designing the algorithms. In this line many works were proposed. Here we are proposing a noise reduction schemeto estimate the noise from ground truth image. We mainly modelled the rician noise in this work.Then we considered modified Dual tree complex Wavelet Transform in the initial step followed by Rotational invariant version of Non-Local Mean filtering with the sparse matrix assumption. The proposed methods were evaluated using the performance metrics Peak signal-to-noise ratio (PSNR) and Image Structural Match Measure (ISMM).