Generative restoration for cardiac PET attenuation correction: a two-stage 3D DDIM framework optimizing fidelity and clinical controllability

This study aims to develop a two-stage 3D denoising diffusion implicit model (DDIM) framework for CT-free attenuation correction in cardiac PET imaging, enabling direct generation of attenuation-corrected (AC) images from non-attenuation-corrected (NAC) PET scans. The method is comprehensively validated using both [18F]FDG PET and [13N]ammonia cardiac PET datasets to demonstrate its clinical applicability across different perfusion and metabolic imaging protocols. The framework employs a two-stage approach: (1) a noise-to-image DDIM was first pretrained on all available AC images (i.e., no need of paired NACs) to learn a diverse AC distributions, enabling the high-fidelity generation of AC images with varying appearances; (2) the pretrained model was fine-tuned with a limited set of paired NAC-AC images to form a conditional DDIM, ensuring anatomically aligned, controllable generation. The model architecture uses a 3D U-Net, trained on 224 paired NAC-AC and 396 unpaired AC images for [18F]FDG, and 608 paired NAC-AC images and 885 unpaired AC images for [13N]ammonia. Performance was evaluated through quantitative metrics (including NMAE, NRMSE, SSIM and PSNR) and visual assessment. The proposed two-stage DDIM framework achieved excellent agreement with clinical CT-based attenuation correction (CT-AC), demonstrating superior correlation (slope = 0.78, $$\:{R}^{2}$$ = 0.95 for [18F]FDG; slope = 0.99, $$\:{R}^{2}$$= 0.91 for [13N]ammonia) and lower errors compared to existing approaches. Ablation studies confirmed the benefits of both the two-stage training strategy and the incorporation of unpaired AC images, as evidenced by narrower confidence intervals in Bland-Altman analysis and reduced percentage errors. The two-stage 3D DDIM framework achieves performance comparable to clinical CT-AC while effectively leveraging unpaired data, demonstrating significant potential for robust cardiac PET attenuation correction.

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Publication Details

Journal
EJNMMI Physics
Published
2026-02-02
DOI
https://doi.org/10.1186/s40658-026-00839-7
Primary Topic
Medical Imaging Techniques and Applications
Type
article
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article

Generative restoration for cardiac PET attenuation correction: a two-stage 3D DDIM framework optimizing fidelity and clinical controllability

Xiaotong Hong, Weiping Xu, Chunfeng Lian, Hao Sun et al.
EJNMMI Physics
Medical Imaging Techniques and Applications
article

Generative restoration for cardiac PET attenuation correction: a two-stage 3D DDIM framework optimizing fidelity and clinical controllability

Xiaotong Hong, Weiping Xu, Chunfeng Lian, Hao Sun, Junhang Deng, Jianhua Ma, Lijun Lu, Fan Wang, Haifeng Wang
article en

Abstract

This study aims to develop a two-stage 3D denoising diffusion implicit model (DDIM) framework for CT-free attenuation correction in cardiac PET imaging, enabling direct generation of attenuation-corrected (AC) images from non-attenuation-corrected (NAC) PET scans. The method is comprehensively validated using both [18F]FDG PET and [13N]ammonia cardiac PET datasets to demonstrate its clinical applicability across different perfusion and metabolic imaging protocols. The framework employs a two-stage approach: (1) a noise-to-image DDIM was first pretrained on all available AC images (i.e., no need of paired NACs) to learn a diverse AC distributions, enabling the high-fidelity generation of AC images with varying appearances; (2) the pretrained model was fine-tuned with a limited set of paired NAC-AC images to form a conditional DDIM, ensuring anatomically aligned, controllable generation. The model architecture uses a 3D U-Net, trained on 224 paired NAC-AC and 396 unpaired AC images for [18F]FDG, and 608 paired NAC-AC images and 885 unpaired AC images for [13N]ammonia. Performance was evaluated through quantitative metrics (including NMAE, NRMSE, SSIM and PSNR) and visual assessment. The proposed two-stage DDIM framework achieved excellent agreement with clinical CT-based attenuation correction (CT-AC), demonstrating superior correlation (slope = 0.78, $$\:{R}^{2}$$ = 0.95 for [18F]FDG; slope = 0.99, $$\:{R}^{2}$$= 0.91 for [13N]ammonia) and lower errors compared to existing approaches. Ablation studies confirmed the benefits of both the two-stage training strategy and the incorporation of unpaired AC images, as evidenced by narrower confidence intervals in Bland-Altman analysis and reduced percentage errors. The two-stage 3D DDIM framework achieves performance comparable to clinical CT-AC while effectively leveraging unpaired data, demonstrating significant potential for robust cardiac PET attenuation correction.

EJNMMI PhysicsVol. 13(1)
Huzhou Normal University (CN), Huzhou Vocational and Technical College (CN), Shanghai Liangyou (China) (CN), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Southern Medical University (CN), Xi'an Jiaotong University (CN)
Sustainable cities and communities
Openalex Percentile: Top 70%
Medical Imaging Techniques and Applications
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