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

Area of Science:

• Neuroimaging
• Developmental Neuroscience
• Medical Image Analysis

Background:

• Ultra-low-field (ULF) MRI offers accessible neuroimaging but faces challenges in infant studies due to low resolution and contrast.
• Existing brain segmentation tools are often unsuitable for ULF infant MRI data, limiting volumetric analysis.

Purpose of the Study:

• To introduce and validate miniMORPH, an open-source pipeline for automated brain volumetry from ULF T2-weighted MRI in infants and toddlers.
• To assess the accuracy and reliability of miniMORPH in segmenting brain tissues and substructures compared to high-field MRI references.

Main Methods:

• Acquired ULF MRI scans from infants (2-27 months) across two cohorts (South Africa, Uganda).
• Utilized age-specific templates and priors for segmenting brain regions.
• Validated segmentations against expert manual and automated high-field (HF) MRI segmentations, quantifying ordering and scaling differences.

Main Results:

• miniMORPH generated plausible brain segmentations across infancy, preserving between-subject ordering in most regions.
• Performance varied by cohort and age, with stronger correspondence in South Africa than Uganda at 12 months.
• Identified systematic scaling offsets in CSF-rich regions and captured age-related growth trajectories and sex- and birthweight-dependent volumetric differences.

Conclusions:

• miniMORPH enables volumetric analysis of ULF infant MRI, preserving variation crucial for developmental and group studies.
• Calibration may be needed for absolute volumes, especially in CSF-rich regions.
• The pipeline is openly available, promoting wider use in infant neuroimaging research.