An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images

Effective denoising is vital for proper analysis and accurate quantitative measurements from Magnetic Resonance (MR) images. Apart from following the general criteria for denoising, the algorithms that deal with MR images should also take into account the bias generated due to the Rician nature of the noise in the magnitude MR images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising MR images. However, one drawback of the existing non local ML estimation method is the usage of a fixed sample size for ML estimation. As a result, optimal results cannot be achieved because of over or under smoothing. In this work, we propose an adaptive non local ML estimation method for denoising MR images in which the samples are selected in an adaptive way for the ML estimation of the true underlying signal. The method has been tested both on simulated and real data, showing its effectiveness.