Imaging NeuroscienceNeurosciencePeer-reviewed journal article
Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI
František VášaiD, Carly BennallickiD, Niall BourkeiD, Francesco PadormoiD, Levente BaljeriD, Ula Briski, Paul CawleyiD, Tomoki ArichiiD, Tobias WoodiD, David J. LythgoeiD, Flavio Dell’AcquaiD, Thomas Calvert BoothiD, Ashwin V. VenkataramaniD, Emil LjungbergiD, Sean DeoniiD, Rosalyn MoraniD, Robert LeechiD, Steven WilliamsiD
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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 (<0.1T) 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.
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