LMGDM: A Lesion-aware Mutual Guidance Diffusion Model with attenuation prior constraint for self-attenuation correction of whole-body PET.
Authors
Affiliations (5)
Affiliations (5)
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China.
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China. Electronic address: [email protected].
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China. Electronic address: [email protected].
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200230, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China. Electronic address: [email protected].
Abstract
Attenuation correction is critical for PET imaging to correctly reflect physiological activity. Recent studies perform PET self-attenuation correction, enabling attenuation correction from PET data itself instead of using additional CT or MRI. These methods either predict attenuation-corrected PET (AC-PET) from non-attenuation-corrected PET (NAC-PET) directly or synthesize intermediate CT images to guide PET attenuation correction. However, cross-modality synthesis of CT from NAC-PET is challenging, especially for small yet clinically important lesions. To address these challenges, we propose the Lesion-aware Mutual Guidance Diffusion Model (LMGDM), a coarse-to-fine dual-branch diffusion model that jointly performs PET attenuation correction and CT synthesis, with particular focus on lesion regions. Specifically, we first generate coarse predictions of both AC-PET and CT using individual residual diffusion models. Subsequently, the coarse AC-PET and CT are jointly refined by a proposed dual-branch mutual guidance module to enable feature fusion of the AC-PET and CT branches. Moreover, a lesion-aware refinement module is embedded into the PET branch, encouraging the network to focus on regions with pathologically high uptake rather than physiologically high uptake. In addition, an attenuation prior learned from real CT images is also introduced to further enhance the fidelity of the synthesized AC-PET and CT. Extensive evaluation on eight data centers demonstrates strong superiority of our LMGDM over the state-of-the-art methods.