Unpaired Image-to-Image Translation of ULF-MRI using Vision Transformers to Advance Volumetric Analyses

Motivation: The image quality of ultra-low-field MRI impacts the reliability of volumetric analysis in the brain. Existing techniques that address this issue learn from synthetically generated images, leading to a domain shift problem when presented with real images. Goal(s): Development of a deep learning method trained with real ULF and HF images to robustly generate an image that can be segmented with routine software tools. Approach: We introduce a CycleGAN framework with Residual Vision Transformers to improve super-resolved images compared to existing methods. Results: The accuracy of volumetric estimations improves using our method compared to others based on clinical correlations and test-retest reliability metrics. Impact: Our new image enhancement method should allow reliable volumetric evaluation using ULF-MRI. This will allow investigators in regions with access to ULF systems to monitor brain health in a way that was previously unattainable.