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<p>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. Data were acquired across multiple scanner software versions, and potential intensity variability was evaluated and mitigated through standardized preprocessing. The resource includes population and age-specific templates with accompanying example segmentations and complete scripts for full reproducibility. Derived template data and code are openly available on Zenodo and GitHub in accordance with FAIR principles. This resource provides ULF-specific spatial priors to support normalization, registration benchmarking, and method development within comparable acquisition settings.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月17日

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  • Kh Tohidul Islam,
  • Parisa Zakavi,
  • Shenjun Zhong,
  • Sanuwani Dayarathna,
  • Himashi Peiris,
  • Helen Kavnoudias,
  • Yi Chao Foong,
  • Anneke Van Der Walt,
  • Juan F. Domínguez D,
  • Karen Caeyenberghs,
  • Gary F. Egan,
  • Meng Law,
  • Zhaolin Chen

摘要

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. Data were acquired across multiple scanner software versions, and potential intensity variability was evaluated and mitigated through standardized preprocessing. The resource includes population and age-specific templates with accompanying example segmentations and complete scripts for full reproducibility. Derived template data and code are openly available on Zenodo and GitHub in accordance with FAIR principles. This resource provides ULF-specific spatial priors to support normalization, registration benchmarking, and method development within comparable acquisition settings.