PADM: A Physics-aware Diffusion Model for Attenuation Correction
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918193584406528 |
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| author | Pham, Trung Kien Vu, Hoang Minh Chu, Anh Duc Nguyen, Dac Thai Nguyen, Trung Thanh Truong, Thao Nguyen Son, Mai Hong Nguyen, Thanh Trung Nguyen, Phi Le |
| author_facet | Pham, Trung Kien Vu, Hoang Minh Chu, Anh Duc Nguyen, Dac Thai Nguyen, Trung Thanh Truong, Thao Nguyen Son, Mai Hong Nguyen, Thanh Trung Nguyen, Phi Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06948 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PADM: A Physics-aware Diffusion Model for Attenuation Correction Pham, Trung Kien Vu, Hoang Minh Chu, Anh Duc Nguyen, Dac Thai Nguyen, Trung Thanh Truong, Thao Nguyen Son, Mai Hong Nguyen, Thanh Trung Nguyen, Phi Le Computer Vision and Pattern Recognition 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. |
| title | PADM: A Physics-aware Diffusion Model for Attenuation Correction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.06948 |