PADM: A Physics-aware Diffusion Model for Attenuation Correction

TL;DR

This paper introduces PADM, a physics-aware diffusion model that corrects attenuation artifacts in cardiac SPECT imaging without the need for CT, leveraging physics priors and a new dataset for improved accuracy.

Contribution

We propose PADM, a novel diffusion-based model with physics-informed training for CT-free attenuation correction in cardiac SPECT imaging.

Findings

PADM outperforms existing generative models in fidelity.

PADM effectively corrects attenuation artifacts using only NAC input.

The CardiAC dataset supports robust training and evaluation.

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.