Keywords: Analysis/Processing, Low-Field MRI, ULF MRI, Ultra-Low-Field MRI, Deep Learning, Unpaired Image Translation, Brain Segmentation, Vision Transformers, CycleGAN
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.
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