Li, Zhiyuan; Gao, Chenyu; Kanakaraj, Praitayini; Bao, Shunxing; Zuo, Lianrui; Kim, Michael E.; Newlin, Nancy R.; Rudravaram, Gaurav; Mohd Khairi, Nazirah Mohd; Huo, Yuankai; Schilling, Kurt G.; Kukull, W. A.; Toga, Arthur W.; Archer, Derek B.; Hohman, Timothy J.; & Landman, Bennett Allan. (2026). Multi-modality conditioned variational U-net for field-of-view extension in brain diffusion MRI. Magnetic Resonance Imaging, 129, 110617. https://doi.org/10.1016/j.mri.2026.110617
In diffusion magnetic resonance imaging, or dMRI, an incomplete field of view (FOV) means that part of the brain is missing from the scan. This can seriously affect analyses of white matter connectivity, including tractography, which maps the pathways of nerve fiber bundles across the brain. Although previous studies have used deep generative models to estimate or “impute” the missing regions, it is still unclear how to best use additional information from paired multi-modality data, such as combining dMRI with structural T1-weighted (T1w) MRI, to improve the quality of reconstruction and support downstream analyses.
To address this, the researchers developed a new framework that imputes missing dMRI regions by integrating diffusion features from the acquired portion of the scan with information about the complete brain anatomical structure derived from paired imaging data. The idea is that using anatomical guidance from other modalities can improve how the missing diffusion signals are reconstructed. They tested the framework on two cohorts from different sites, including a total of 96 participants, and compared it with a baseline method that treated T1w and dMRI information equally without specifically leveraging their complementary roles.
The proposed framework significantly improved imputation quality, as measured by the angular correlation coefficient, and improved the accuracy of downstream tractography, as measured by the Dice score. These results suggest that carefully integrating paired multi-modality data leads to more accurate reconstruction of incomplete dMRI scans. By improving whole-brain tractography, this approach may reduce uncertainty in analyses of white matter bundles, particularly those relevant to neurodegenerative diseases.
Fig. 1.
Visualization (left) and histogram (right) of 103 real cases of dMRI scans with incomplete FOV that failed quality assurance. In the left figure, horizontal regions indicate the distribution of the incomplete part of FOV with an estimated position of a brain mask. The total cutoff distance from the incomplete FOV to the top of the brain is estimated using a corresponding and registered T1w image. Its histogram is presented in the right figure.