A novel Split Bregman algorithm for MRI denoising task in an e-Health system

Abstract

An interesting challenge in e-Health is to develop tools and software in order to benefit the healthcare services. Our applicative context is Magnetic Resonance Imaging (MRI). The main purpose of this paper is to propose a regularization framework for solving an inverse reconstruction problem in MRI. We focus on the Split Bregman method, which is a well known efficient tool for solving a wide variety of optimization problems e.g. total variation minimization problems arising from image denoising. The proposed denoising approach, based on the TV/ROF model, involves a second-order derivative penalty term and, accordingly, introduces some modifications to the Split Bregman scheme. Our iterative regularization strategy has interesting features in highlighting the image contrasts and in the noise removal. Numerical experiments prove the goodness of the proposed approach.