Ultra-low-field brain MRI morphometry: Test–retest reliability and correspondence to high-field MRI

  • František Váša
  • , Carly Bennallick
  • , Niall J. Bourke
  • , Francesco Padormo
  • , Levente Baljer
  • , Ula Briski
  • , Paul Cawley
  • , Tomoki Arichi
  • , Tobias C. Wood
  • , David J. Lythgoe
  • , Flavio Dell’Acqua
  • , Thomas C. Booth
  • , Ashwin V. Venkataraman
  • , Emil Ljungberg
  • , Sean C.L. Deoni
  • , Rosalyn J. Moran
  • , Robert Leech
  • , Steven C.R. Williams

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Magnetic resonance imaging (MRI) enables non-invasive monitoring of healthy brain development and disease. Widely used higher field (>1.5 T) MRI systems are associated with high energy and infrastructure requirements, and high costs. Recent ultra-low-field (<0.1 T) systems provide a more accessible and cost-effective alternative. However, it remains uncertain whether anatomical ultra-low-field neuroimaging can be used to reliably extract quantitative measures of brain morphometry, and to what extent such measures correspond to high-field MRI. Here we scanned 23 healthy adults aged 20–69 years on two identical 64 mT systems and a 3 T system, using T1w and T2w scans across a range of (64 mT) resolutions. We segmented brain images into 4 global tissue types and 98 local structures, and systematically evaluated between-scanner reliability of 64 mT morphometry and correspondence to 3 T measurements, using correlations of tissue volume and Dice spatial overlap of segmentations. We report high 64 mT reliability and correspondence to 3 T across 64 mT scan contrasts and resolutions, with highest performance shown by combining three T2w scans with low through-plane resolution into a single higher-resolution scan using multi-resolution registration. Larger structures show higher 64 mT reliability and correspondence to 3 T. Finally, we showcase the potential of ultra-low-field MRI for mapping neuroanatomical changes across the lifespan, and monitoring brain structures relevant to neurological disorders. Raw images are publicly available, enabling systematic validation of pre-processing and analysis approaches for ultra-low-field neuroimaging.

Original languageEnglish
Article numberIMAG.a.930
JournalImaging Neuroscience
Volume3
DOIs
StatePublished - 15 Oct 2025
Externally publishedYes

Keywords

  • anatomy
  • low- and middle-income countries (LMIC)
  • magnetic resonance imaging
  • morphometry
  • segmentation
  • ultra-low-field

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