Abstract

Motivation: Diffusion MRI (dMRI) is promising for predicting disabilities due to neonatal hypoxic-ischemic encephalopathy (HIE), yet current automated image quantification methods are slow and unvalidated for HIE lesions. Goal(s): Develop a rapid deep-learning model, OpenMAP-Di, to quantify dMRI with and without HIE injury to predict the short-term outcome (STO) score. Approach: We utilized nnU-Net to develop OpenMAP-Di, enabling dMRI parcellation and quantification, and applied an elastic regression model to predict the STO score. Results: OpenMAP-Di accurately parcellated and quantified infant brains across varying scanners, acquisition parameters, and HIE severity levels in three minutes, and can also predict STO. Impact: The increased processing speed and robustness to technological and pathological variations offered by OpenMAP-Di promises timely and reliable future neurodevelopmental outcome assessments for individuals surviving HIE, while also offering researchers opportunities for extensive medical image analysis.