Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (FOV). This work aims 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 complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWI) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWI outside of incomplete FOV. Results: For evaluating the imputed slices, on the WRAP dataset the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, SSIMb1300=0.893; on the NACC dataset it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, 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. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in dMRI data with incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's Disease.

翻译：目的：在扩散磁共振成像中，不完整的视野会严重阻碍全脑组织微结构和连接性的体积分析与纤维束分析。本研究旨在开发一种方法，可直接从存在视野缺失的扩散磁共振扫描数据中填充缺失切片。我们假设填充完整视野的图像能够改善因视野不完整而受损数据的全脑纤维束追踪效果。因此，本方法为保留原本需丢弃的宝贵扩散磁共振数据提供了更优替代方案，使得后续原本难以或无法通过受损数据实现的纤维束追踪分析成为可能。方法：我们提出基于深度生成模型的框架，用于估算扩散磁共振扫描中视野缺失的脑区。该模型能够同时学习弥散加权图像中的扩散特性与对应结构图像中的解剖特征，从而有效填充视野范围外的弥散加权图像缺失切片。结果：在WRAP数据集上，填充切片的评估结果为PSNRb0=22.397、SSIMb0=0.905、PSNRb1300=22.479、SSIMb0=0.893；在NACC数据集上达到PSNRb0=21.304、SSIMb0=0.892、PSNRb1300=21.599、SSIMb1300=0.877。在WRAP和NACC两个数据集的72条纤维束平均Dice系数均显著提升（p<0.001），表明本框架有效提高了纤维束追踪精度。结论：结果表明，本框架能够对视野不完整的扩散磁共振数据实现充分填充，从而改善全脑纤维束追踪效果，修复受损数据。通过扩展并补全视野，本方法获得了更精确的全脑纤维束追踪结果，并降低了与阿尔茨海默病相关纤维束分析的不确定性。