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Abstract #1123

Rescuing Incomplete MR Data: Anatomy Imputation of Restricted Field of View Images Using Multi-Contrast MR Images

Savannah P Hays1, Samuel W Remedios2, Lianrui Zuo3, Jinwei Zhang1, Aaron Carass1, Ellen M Mowry4, Scott D Newsome4, Jerry L Prince1, and Blake E Dewey4
1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, 2Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States, 3Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, 4Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence

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.

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