AI-powered 0.3 mm Ultrahigh Resolution MR Brain Imaging

Motivation: Ultrahigh-resolution MRI is very challenging due to long scan time and signal-to-noise trade-offs. Machine learning provides new opportunities but, to our knowledge, has not been demonstrated due to limited training data available, huge computational demands, and potential morphological distortions. Goal(s): To achieve generalizable ultrahigh-resolution MR brain imaging at 0.3 mm, using very limited data. Approach: We proposed a diffusion bridge with model-based fake feature correction using 0.3 mm priors from one brain image and 1.0 mm priors from 10,000 brain images. Results: Our approach successfully produced high-quality brain images at 0.3 mm, which were validated on both 13 public datasets and stroke patients. Impact: Conventional MRI scans of the brain are typically done at 1 mm resolution. Ultrahigh-resolution MRI will open up many opportunities for research and clinical applications. The proposed approach may also be useful for solving other imaging and processing problems.