GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction
This work addresses PET reconstruction for molecular imaging, offering improved image quality and detail preservation, but it appears incremental as it combines existing techniques (GR and diffusion models) for a specific application.
The authors tackled the problem of 3D low-dose whole-body PET reconstruction, which suffers from noise, blurring, and detail loss, by proposing GR-Diffusion, a framework that integrates 3D Gaussian representation with diffusion models, resulting in outperforming state-of-the-art methods on UDPET and Clinical datasets across varying dose levels.
Positron emission tomography (PET) reconstruction is a critical challenge in molecular imaging, often hampered by noise amplification, structural blurring, and detail loss due to sparse sampling and the ill-posed nature of inverse problems. The three-dimensional discrete Gaussian representation (GR), which efficiently encodes 3D scenes using parameterized discrete Gaussian distributions, has shown promise in computer vision. In this work, we pro-pose a novel GR-Diffusion framework that synergistically integrates the geometric priors of GR with the generative power of diffusion models for 3D low-dose whole-body PET reconstruction. GR-Diffusion employs GR to generate a reference 3D PET image from projection data, establishing a physically grounded and structurally explicit benchmark that overcomes the low-pass limitations of conventional point-based or voxel-based methods. This reference image serves as a dual guide during the diffusion process, ensuring both global consistency and local accuracy. Specifically, we employ a hierarchical guidance mechanism based on the GR reference. Fine-grained guidance leverages differences to refine local details, while coarse-grained guidance uses multi-scale difference maps to correct deviations. This strategy allows the diffusion model to sequentially integrate the strong geometric prior from GR and recover sub-voxel information. Experimental results on the UDPET and Clinical datasets with varying dose levels show that GR-Diffusion outperforms state-of-the-art methods in enhancing 3D whole-body PET image quality and preserving physiological details.