Adaptive Bayesian filtering based restoration of MR images

Abstract

MR images contain noise and for quantitative clinical diagnosis restoration is essential. For single coil MR images, noise follows Rician distribution when signal to noise ratio (SNR) is low and Gaussian distribution when SNR is high. Rician noise is signal dependent and introduces bias. The work proposes an adaptive Bayesian framework for restoration of 2D magnitude MR images. Restoration is achieved by Rician likelihood as the data attachment term with range and domain Gaussian filters, adaptive to noise as prior in Maximum A´ posterior framework. A good filtering behavior is achieved due to the domain component of the filter and crisp edges are preserved at the same time due to the noise adaptive range component. Rician likelihood aids the image restoration in terms of bias removal. Convergence of the proposed method further highlights the optimal filtering performance. Experiments conducted on publically available Brainweb phantom demonstrate enhanced performance in terms of signal to noise ratio, structural similarity index and overall performance.