Evaluating Template Generation Methods for Spatially Normalizing Down Syndrome Brain Magnetic Resonance Images

Abstract

AbstractBackgroundMagnetic resonance (MR) image registration and segmentation algorithms commonly used in studies of neurodegeneration have a higher failure rate for Down syndrome (DS) populations due to anatomical and morphological differences. For example, the Desikan‐Killiany atlas used by FreeSurfer was defined using MR images from cognitively normal adults and patients with Alzheimer’s disease (AD). The aim of this work was to evaluate three template generation methods (or group‐wise image registration) for spatially matching DS MR images and generating a DS cohort‐specific template.Method138 scans of DS subjects from the NiAD consortium were employed. The three template generation methods evaluated were DARTEL, SHOOT, and SyGN. DARTEL and SHOOT are implemented in the SPM12 (v7771) software package and operate on tissue segmentation maps. SyGN is implemented in ANTs software and operates on MR images. The data processing pipeline is presented in Figure 1. Scan‐specific grey matter (GM) and white matter (WM) tissue probability maps were generated using SPM12’s Unified Segmentation method for DARTEL and SHOOT, and MR images were skull‐stripped for SyGN. To evaluate spatial matching, all MR images were warped to each template space using method‐specific deformation fields and an average MR image was generated specific to each template method. Structural similarity index measure (SSIM) and Normalized Mutual Information (NMI) metrics were calculated on a scan specific basis relative to the average MR image for each template method. Lower SSIM and NMI measures effectively reflect greater spatial variability across DS scans and therefore worse spatial matching performance.ResultThe DARTEL and SHOOT tissue map (GM and WM) templates and SyGN MR template are presented in Figure 2. Average MRs for each template method are presented in Figure 3. Consistent relationships were observed between the 3 template generation methods for the mean (SHOOT > SyGN > DARTEL) and standard deviation (SyGN < DARTEL < SHOOT) of SSIM and NMI (Figures 4 and 5, Table 1).ConclusionWith relatively high means and the lowest standard deviation, SyGN produced robust spatial matching across all scans. Future Down syndrome MRI and PET image processing will make use of the SyGN template.