Preterm neonatal diffusion processing using detection and replacement of outliers prior to resampling

Abstract

In diffusion weighted MRI, subject motion and brain pulsation lead both to signal drop-outs and image misalignment. Unsedated neonates, with their higher heart rate and propensity for motion are particularly prone to degraded scan quality that impairs diffusion tensor estimation. Retrospective registration and robust estimators are two methods that have previously been demonstrated to address motion and intensity outliers, respectively, in diffusion data. However, when taken together, the resampling of images to correct for misalignment can have the effect of averaging outlier voxels with uncorrupted voxels, thereby making outliers more difficult to detect. This article presents a method to remove outliers prior to resampling while taking misalignment into account so that this averaging of outliers with good data can be avoided. The proposed method is compared to other processing pipelines using simulations and data from unsedated preterm neonates. These results demonstrate advantages to the proposed method, particularly in subjects with high motion.