MiniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants

Motivation: Infancy is a critical period for brain development. MRI provides a window into the developing brain, but its access is limited in low- and middle-income countries (LMICs). Portable ultra-low-field (ULF) MRI improves accessibility, yet low contrast, resolution, and signal-to-noise ratio challenge current morphometry tools. Goal(s): To develop a pipeline for quantifying brain volumes in infant ULF MRI data, supporting neurodevelopmental research globally. Approach: We built an atlas-guided tissue segmentation that accurately estimates tissue volumes in low-contrast infant ULF brain MRI. Results: Our pipeline, MiniMORPH, successfully segmented ULF brain images, revealing significant effects of age, sex, and birthweight on tissue volumes. Impact: We provide a scalable, open-source solution for segmentation of ULF MRI infant data, unlocking new possibilities for assessing neurodevelopment in diverse settings.