Keywords: AI Diffusion Models, AI/ML Image Reconstruction
Motivation: Cardiac CINE and EPI MRI sequences rely on minimal motion during acquisition. Irregular heartbeats and patient movement disrupt k-space sampling, resulting in signal dropout that compromises clinical assessment.
Goal(s): Develop a method to restore corrupted slices in 3D MRI volumes while maintaining anatomical consistency.
Approach: Implemented a 3D self-supervised network that restores corrupted slices using Denoising Diffusion Probabilistic Model with posterior sampling, which does not require corruption model during training.
Results: The method achieved superior accuracy compared to conventional techniques, demonstrating lower NRMSE (0.013±0.003 vs 0.036±0.017) and higher PSNR (38.6±1.7 vs 29.8±2.7) across cardiac structures compared to Deep Image Prior and cubic interpolation.
Impact: The demonstrated combination of diffusion posterior sampling with self-supervised learning establishes a framework for artifact-robust medical image restoration. This advances both computational efficiency in MRI post-processing and enables new research into automated quality assessment of cardiac functional metrics.
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