A multi-dimensional non-linear edge-preserving filter for magnetic resonance image restoration

The authors present a multi-dimensional non-linear edge-preserving filter for restoration and enhancement of magnetic resonance images (MRI). The filter uses both of the inter-frame (parametric or temporal) and intra-frame (spatial) information to filter the additive noise from an MRI scene sequence. It combines the approximate maximum likelihood (equivalently, least squares) estimate of the inter-frame pixels, using MRI signal models, with a trimmed spatial smoothing algorithm, using a Euclidean distance discriminator to preserve partial volume and edge information. The filter´s structure is parallel, making its implementation on a parallel processing computer trivial. Details of the filter implementation for a sequence of four multiple spin-echo images is explained, and the effects of filter parameters (neighborhood size and threshold value) on the computation time and performance of the filter is discussed. The filter is applied to MRI simulation and brain studies, serving as a pre-processing procedure for the eigenimage filter. It outperforms conventional pre- and post-processing filters, including spatial smoothing, low-pass filtering with a Gaussian kernel, median filtering, and combined vector median with average filtering