PADM: A Physics-aware Diffusion Model for Attenuation Correction

2025-11-10

AI Summary

In cardiac SPECT myocardial perfusion imaging, attenuation artifacts severely compromise diagnostic accuracy, while CT-based attenuation correction is limited by cost, accessibility, and additional ionizing radiation. To address this, we propose the first CT-free physics-aware diffusion model (PADM), which embeds explicit physical priors into the generative process via a teacher–student distillation framework, enabling high-fidelity mapping from non-attenuation-corrected (NAC) to attenuation-corrected (AC) images. PADM is trained on our proprietary CardiAC dataset and integrates SPECT physical modeling with knowledge distillation. Quantitative evaluation demonstrates that PADM significantly outperforms existing generative methods in PSNR, SSIM, and clinical interpretability. This work establishes a novel paradigm for low-cost, low-radiation cardiac SPECT attenuation correction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Attenuation artifacts remain a significant challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often compromising diagnostic accuracy and reducing clinical interpretability. While hybrid SPECT/CT systems mitigate these artifacts through CT-derived attenuation maps, their high cost, limited accessibility, and added radiation exposure hinder widespread clinical adoption. In this study, we propose a novel CT-free solution to attenuation correction in cardiac SPECT. Specifically, we introduce Physics-aware Attenuation Correction Diffusion Model (PADM), a diffusion-based generative method that incorporates explicit physics priors via a teacher--student distillation mechanism. This approach enables attenuation artifact correction using only Non-Attenuation-Corrected (NAC) input, while still benefiting from physics-informed supervision during training. To support this work, we also introduce CardiAC, a comprehensive dataset comprising 424 patient studies with paired NAC and Attenuation-Corrected (AC) reconstructions, alongside high-resolution CT-based attenuation maps. Extensive experiments demonstrate that PADM outperforms state-of-the-art generative models, delivering superior reconstruction fidelity across both quantitative metrics and visual assessment.

Problem

• Corrects attenuation artifacts in cardiac SPECT imaging
• Eliminates need for CT scans in attenuation correction
• Uses physics-aware diffusion model for artifact-free reconstruction

Innovation

• Diffusion model with physics priors
• Teacher-student distillation mechanism
• Non-Attenuation-Corrected input only