Training Diffusion Probabilistic Models with Limited Data for Accelerated MRI Reconstruction with Application to Stroke MRI

Motivation: Faster MRI reduces motion artifacts, costs, and time to treatment, crucial for stroke diagnosis; however, current protocols are lengthy, often making CT the preferred option. Goal(s): To accelerate the stroke MRI protocol using diffusion probabilistic models, reducing scan time by half. Approach: A foundation model is initially trained on a large public dataset, followed by fine-tuning on a smaller, contrast-specific dataset with a decayed learning rate and a short training period. Results: The proposed method applied to retrospective data supports a 50% reduction in scan time, with improvements validated through both numerical error metrics and a qualitative neurologist assessment. Impact: Training diffusion probabilistic models with limited data across various MRI contrasts holds substantial potential to accelerate diverse MRI protocols, addressing a critical unmet need in time-sensitive care scenarios, such as stroke diagnosis.