Motivation: Variations in clinical magnetic resonance (MR) imaging, due to differences in hardware and acquisition parameters, often result in inconsistencies in image contrast and field-of-view, complicating analysis across imaging sites. Goal(s): To develop a method to impute missing regions of MR images of the brain due to a restricted field-of-view during harmonization. Approach: We expanded on an existing disentangling harmonization framework to differentiate between anatomical (foreground) and non-anatomical (background) regions. Results: Our approach successfully imputed the anatomy for restricted field-of-view MR images while simultaneously harmonizing contrast. Impact: Our results impact researchers handling diverse, inconsistent imaging datasets with variable field-of-view acquisitions. This approach enables the analysis of previously unusable data by imputing missing regions using multi-contrast information, making them suitable for meaningful clinical or research outcomes.
Hays et al. (Tue,) studied this question.