MiniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants
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Background
Ultra-low-field (ULF) MRI facilitates neuroimaging access, yet its application in early infancy is constrained by low resolution/contrast, and the limited suitability of existing segmentation tools.
Objective
We present miniMORPH, an open-source pipeline for infant brain volumetry from 0.064T T2-weighted MRI scans. We evaluate its performance across multiple sites and ages and validate findings against high-field (HF) segmentations.
Methods
ULF scans were acquired from infants (2 to 27 months) across two cohorts in South Africa and Uganda. Age-specific templates and priors from established neurodevelopmental atlases were used to delineate brain tissues and substructures. Reliability was assessed by examining age, sex, and birthweight effects, and by comparing volumes to those from matched HF scans.
Results
miniMORPH successfully segmented major brain regions, capturing age-related growth trajectories. Sex-dependent volumetric differences were widespread but attenuated after intracranial volume correction. Low birthweight infants exhibited reduced regional volumes and altered growth trajectories. ULF-HF comparison showed strong correlations in most structures, although systematic biases were observed in ventricular estimates. Performance varied by age, with greatest segmentation deviation at 3 months.
Conclusion
miniMORPH enables reliable volumetric analysis of ULF infant MRI. Its compatibility with ULF systems supports equitable global neurodevelopmental research. The pipeline is openly available at https://github.com/UNITY-Physics/fw-minimorph .
