Automated filter selection for suppression of Gibbs ringing artefacts in MRI

Abstract

Gibbs ringing creates artefacts in magnetic resonance images that can mislead clinicians. Reconstruction algorithms attempt to suppress Gibbs ringing, or an additional ringing suppression algorithm may be applied post reconstruction. Novel reconstruction algorithms are often compared with filtered Fourier reconstruction, but the choices of filters and filter parameters can be arbitrary and sub-optimal. Evaluation of different reconstruction and post-processing algorithms is difficult to automate or subjective: many metrics have been used in the literature. In this paper, we evaluate twelve of those metrics and demonstrate that none of them are fit for purpose. We propose a novel metric and demonstrate its efficacy in 1D and 2D simulations. We use our new metric to optimise and compare 17 smoothing filters for suppression of Gibbs artefacts. We examine the transfer functions of the optimised filters, with counter-intuitive results regarding the highest-performing filters. Our results will simplify and improve the comparison of novel MRI reconstruction and post-processing algorithms, and lead to the automation of ringing suppression in MRI. They also apply more generally to other applications in which data is captured in the Fourier domain.