GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction

Fuente: arXiv
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Autores principales: Geng, Mengxiao, Chen, Zijie, Hong, Ran, Li, Bingxuan, Liu, Qiegen
Formato: Preprint
Publicado: 2026
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author Geng, Mengxiao
Chen, Zijie
Hong, Ran
Li, Bingxuan
Liu, Qiegen
author_facet Geng, Mengxiao
Chen, Zijie
Hong, Ran
Li, Bingxuan
Liu, Qiegen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2602_11653
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction
Geng, Mengxiao
Chen, Zijie
Hong, Ran
Li, Bingxuan
Liu, Qiegen
Computer Vision and Pattern Recognition
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.
title GR-Diffusion: 3D Gaussian Representation Meets Diffusion in Whole-Body PET Reconstruction
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2602.11653