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Abstract #3819

3D consistent data restoration for corrupted MRI slices using DDPMs with posterior sampling

Serge Vasylechko1, Onur Afacan1, and Sila Kurugol1
1Boston Children's Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

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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Keywords