Inpainting as a Technique for Estimation of Missing Voxels in Chemical Shift Imaging

Abstract

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI), can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). We applied these techniques to N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. Inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (ps<0.001). Inpainting maintained its superiority whether the previously unusable voxels were randomly distributed or located in regions most commonly affected by voxel loss in real-world data. Clinical RelevanceThe presence of missing voxels can be problematic, particularly when data are analyzed in standard space, given that only voxels that are contributed to by all participants can be interrogated in these analyses. Inpainting is a promising approach for recovering unusable or missing voxels in voxelwise analyses, particularly in imaging modalities characterized by low SNR such as CSI.