AUTHOR=Islam Kh Tohidul , Zhong Shenjun , Zakavi Parisa , Kavnoudias Helen , Farquharson Shawna , Durbridge Gail , Barth Markus , Dwyer Andrew , McMahon Katie L. , Parizel Paul M. , McIntyre Richard , Egan Gary F. , Law Meng , Chen Zhaolin 

TITLE=AI improves consistency in regional brain volumes measured in ultra-low-field MRI and 3T MRI

JOURNAL=Frontiers in Neuroimaging

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2025.1588487

DOI=10.3389/fnimg.2025.1588487

ISSN=2813-1193

ABSTRACT=This study compares volumetric measurements of various brain regions using different magnetic resonance imaging (MRI) modalities and deep learning models, specifically 3T MRI, ultra-low field (ULF) MRI at 64mT, and AI-enhanced ULF MRI using SynthSR and HiLoResGAN. The aim is to evaluate the alignment and agreement among field strengths and ULF MRI with and without AI. Descriptive statistics, paired t-tests, effect size analyses, and regression analyses are employed to assess the relationships and differences between modalities. The results indicate that volumetric measurements derived from 64mT MRI deviate significantly from those obtained using 3T MRI. By leveraging SynthSR and LoHiResGAN models, these deviations are reduced, bringing the volumetric estimates closer to those obtained from 3T MRI, which serves as the reference standard for brain volume quantification. These findings highlight that deep learning models can reduce systematic differences in brain volume measurements across field strengths, providing potential solutions to minimize bias in imaging studies.