Abstract #2531

OpenMAP-Di: Open Resource for Multiple Anatomical Region Parcellation of Diffusion MRI for Infantile Hypoxic-Ischemic Lesion Quantification

Kengo Onda¹, Nathanael Kuo², Kei Nishimaki^3,4, Jill Chotiyanonta³, Yukako Kawasaki⁵, Linda Chang⁶, Thomas Ernst⁶, Charlamaine Parkinson^7,8, Aylin Tekes¹, Raul Chavez-Valdez^7,8, Dhananjay Vaidya⁹, Ernest M Graham¹⁰, Allen D Everett⁸, Frances J Northington^7,8, and Kenichi Oishi¹

¹Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Applied Physics Laboratory, The Johns Hopkins University, Laurel, MD, United States, ³Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁴Applied Informatics, Graduate School of Science and Engineering, Hosei University, Tokyo, Japan, ⁵Neonatology, Toyama University Hospital, Toyama, Japan, ⁶University of Maryland School of Medicine, Baltimore, MD, United States, ⁷Neonatology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁸Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁹General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ¹⁰Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Neuro, Diffusion Tensor Imaging

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.

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