Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction

• URL: http://arxiv.org/abs/2512.22237v1
• Date: Tue, 23 Dec 2025 13:02:18 GMT
• Title: Meta-information Guided Cross-domain Synergistic Diffusion Model for Low-dose PET Reconstruction
• Authors: Mengxiao Geng, Ran Hong, Xiaoling Xu, Bingxuan Li, Qiegen Liu,
• Abstract summary: We introduce a meta-information guided cross-domain synergistic diffusion model (MiG-DM)<n>MiG-DM integrates comprehensive cross-modal priors to generate high-quality PET images.<n>Experiments on the UDPET public dataset and clinical datasets with varying dose levels demonstrate that MiG-DM outperforms state-of-the-art methods in enhancing PET image quality and preserving physiological details.
• Score: 9.752203949076216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics knowledge and patient-specific meta-information, which are critical for functional-semantic correlation mining. In this study, we introduce a meta-information guided cross-domain synergistic diffusion model (MiG-DM) that integrates comprehensive cross-modal priors to generate high-quality PET images. Specifically, a meta-information encoding module transforms clinical parameters into semantic prompts by considering patient characteristics, dose-related information, and semi-quantitative parameters, enabling cross-modal alignment between textual meta-information and image reconstruction. Additionally, the cross-domain architecture combines projection-domain and image-domain processing. In the projection domain, a specialized sinogram adapter captures global physical structures through convolution operations equivalent to global image-domain filtering. Experiments on the UDPET public dataset and clinical datasets with varying dose levels demonstrate that MiG-DM outperforms state-of-the-art methods in enhancing PET image quality and preserving physiological details.

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