PurposeIn brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.ApproachWe propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.ResultsFor evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, and SSIMb1300=0.893; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, and SSIMb1300=0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p<0.001) on both the WRAP and NACC datasets.ConclusionsResults suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

Field-of-view extension for brain diffusion MRI via deep generative models