Abstract #1203

Volumetric Measurement Comparisons for Deep Learning Improved Ultra-Low Field MRI

Kh Tohidul Islam¹, Shenjun Zhong¹, Parisa Zakavi¹, Helen Kavnoudias^2,3, Shawna Farquharson⁴, Gail Durbridge⁵, Markus Barth⁶, Katie L. McMahon⁷, Paul M. Parizel^8,9, Andrew Dwyer^10,11, Gary F. Egan¹, Meng Law^2,12, and Zhaolin Chen^1,13

¹Monash Biomedical Imaging, Monash University, Clayton, Australia, ²Department of Neuroscience, Monash University, Clayton, Australia, ³Surgery, Monash University, Clayton, Australia, ⁴Australian National Imaging Facility, Queensland, Australia, ⁵Herston Imaging Research Facility, University of Queensland, Queensland, Australia, ⁶School of Electrical Engineering and Computer Science, University of Queensland, Queensland, Australia, ⁷School of Clinical Science, Queensland University of Technology, Queensland, Australia, ⁸David Hartley Chair of Radiology, Royal Perth Hospital, Western Australia, Australia, ⁹Medical School, University of Western Australia, Western Australia, Australia, ¹⁰South Australian Health and Medical Research Institute, South Australia, Australia, ¹¹SA Medical Imaging, SA Health, South Australia, Australia, ¹²Radiology, Alfred Hospital, Victoria, Australia, ¹³Data Science and AI, Monash University, Clayton, Australia

Synopsis

Keywords: Analysis/Processing, AI/ML Image Reconstruction, Ultra-low-field MRI, Deep learning, Volumetric measurements, SynthSR, LoHiResGAN

Motivation: Accurate brain volumetrics from ultra-low-field MRI remains challenging, yet reliable brain region segmentation and volume estimates would be valuable in point-of-care settings and for monitoring neurological conditions in underserved areas. This study evaluates deep-learning models to expand ultra-low-field MRI’s clinical and research applications where high-field MRI is unfeasible.

Goal(s): To evaluate if deep-learning models can enhance ultra-low-field MRI images to match high-field MRI volumetric accuracy.

Approach: Applied SynthSR and LoHiResGAN models to ultra-low-field 64mT MRI scans from 92-participants, comparing volumetric measurements of 19-brain regions to those from 3T MRI.

Results: Enhanced images showed significantly improved volumetric accuracy, closely aligning with high-field MRI measurements.

Impact: SynthSR and LoHiResGAN were evaluated for their unique approaches to enhancing ultra-low-field MRI images. Their ability to improve volumetric accuracy highlights their potential to support and expand access to high-quality neuroimaging in settings with limited access to high-field MRI.

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