AIM-ZS: Attention-driven Image reconstruction and Motion estimation using Zero-Shot self-supervised learning for dynamic MRI

Motivation: Deep learning-based motion estimation/correction and reconstruction for dynamic MRI often require pre-training models, which leads to limitations in their application and raises concerns about domain shift. Goal(s): To develop and evaluate a zero-shot self-supervised learning framework that can perform both motion estimation and image reconstruction for dynamic MRI without pre-training. Approach: The proposed AIM-ZS iteratively reconstructs and estimates motion from dynamic undersampled k-space data. It combines reconstruction and motion estimation modules with an attention mechanism. Results: AIM-ZS demonstrated superior performance compared to conventional methods, maintaining high image quality even at high acceleration factors (AF=18), with significantly better PSNR values. Impact: This pre-training-free approach enables the widespread adoption of motion-corrected dynamic MRI without requiring large training datasets. It potentially expands clinical applications requiring high temporal resolution imaging and motion analysis and improving accessibility across different imaging protocols.