Adaptive total variation based filtering for MRI images with spatially inhomogeneous noise and artifacts

The widely adopted total variation (TV) filter is not optimal for MRI images with spatially varying noise levels, not to say those with also artifacts. To better preserve edges and fine structures while sufficiently removing noise and artifacts, we first use local mutual information together with k-means segmentation to automatically locate most of the reliable edges from the noisy input; noise and artifacts distribution at other regions are then studied using local variance; all obtained transparent information in turn guides fully automatic local adjustment of the TV filter. The proposed spatially adaptive TV model has been applied to partially parallel MRI (PP-MRI) image reconstructed using GRAPPA and SENSE. Comparison with Perona-Malik anisotropic diffusion and another adaptive TV verifies that the proposed model provides higher peak signal to noise ratio (PSNR) and results closer to ground truth. Numerical results on many in vivo clinical data sets demonstrate the robustness and viability of the unsupervised method.