Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, ZeroShot Self-Supervised learning, 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.
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