Population-based Brain Templates for Ultra-Low-Field MRI

This repository contains the derived datasets accompanying the publication:

Islam, K.T., Zakavi, P., Zhong, S. et al. Population-based Brain Templates for Ultra-Low-Field MRI. Sci Data (2026). https://doi.org/10.1038/s41597-026-07622-4

Ultra-low-field (ULF) MRI currently lacks population-representative spatial priors, limiting robust alignment, normalization, and cross-study comparability. Our aim is to provide an open, standardized ULF brain template resource, comprising data and code, that enables reproducible spatial analysis and method development across ULF studies. We present group-average brain templates generated from 64 mT MRI scans of 100 healthy adults, encompassing both T1- and T2-weighted contrasts and covering the full adult lifespan. Participants were stratified into three age groups to support both age-specific and population-level analyses. The preprocessing and registration pipeline employed established open-source neuroimaging tools and iterative averaging to enhance anatomical correspondence and consistency. The resource includes population and age-specific templates with accompanying example segmentations and complete scripts for full reproducibility. All data and code are openly available on Zenodo and GitHub in accordance with FAIR principles. By establishing a standardized reference space tailored to ULF imaging, this dataset facilitates normalization, morphometry, and benchmarking, and supports downstream applications such as segmentation, harmonization, and learning-based enhancement.