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

TL;DR

This paper introduces MiG-DM, a novel diffusion model that leverages patient-specific meta-information and cross-domain processing to improve low-dose PET image reconstruction, reducing radiation exposure while maintaining image quality.

Contribution

The study presents a meta-information guided cross-domain diffusion framework that integrates clinical parameters and physical priors for superior PET reconstruction.

Findings

MiG-DM outperforms existing methods in image quality and detail preservation.

The model effectively incorporates patient-specific meta-information.

Experimental results validate the approach on public and clinical datasets.

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.