Yu Boxiao, Ozdemir Savas, Dong Yafei, Shao Wei, Pan Tinsu, Shi Kuangyu, Gong Kuang
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Department of Radiology, University of Florida, Jacksonville, FL, USA.
Eur J Nucl Med Mol Imaging. 2025 Jun;52(7):2549-2562. doi: 10.1007/s00259-025-07122-4. Epub 2025 Feb 6.
Whole-body PET imaging plays an essential role in cancer diagnosis and treatment but suffers from low image quality. Traditional deep learning-based denoising methods work well for a specific acquisition but are less effective in handling diverse PET protocols. In this study, we proposed and validated a 3D Denoising Diffusion Probabilistic Model (3D DDPM) as a robust and universal solution for whole-body PET image denoising.
The proposed 3D DDPM gradually injected noise into the images during the forward diffusion phase, allowing the model to learn to reconstruct the clean data during the reverse diffusion process. A 3D convolutional network was trained using high-quality data from the Biograph Vision Quadra PET/CT scanner to generate the score function, enabling the model to capture accurate PET distribution information extracted from the total-body datasets. The trained 3D DDPM was evaluated on datasets from four scanners, four tracer types, and six dose levels representing a broad spectrum of clinical scenarios.
The proposed 3D DDPM consistently outperformed 2D DDPM, 3D UNet, and 3D GAN, demonstrating its superior denoising performance across all tested conditions. Additionally, the model's uncertainty maps exhibited lower variance, reflecting its higher confidence in its outputs.
The proposed 3D DDPM can effectively handle various clinical settings, including variations in dose levels, scanners, and tracers, establishing it as a promising foundational model for PET image denoising. The trained 3D DDPM model of this work can be utilized off the shelf by researchers as a whole-body PET image denoising solution. The code and model are available at https://github.com/Miche11eU/PET-Image-Denoising-Using-3D-Diffusion-Model .
全身正电子发射断层扫描（PET）成像在癌症诊断和治疗中起着至关重要的作用，但图像质量较低。传统的基于深度学习的去噪方法在特定采集情况下效果良好，但在处理多种PET协议时效果较差。在本研究中，我们提出并验证了一种三维去噪扩散概率模型（3D DDPM），作为全身PET图像去噪的一种强大且通用的解决方案。
所提出的3D DDPM在正向扩散阶段逐渐向图像中注入噪声，使模型在反向扩散过程中学习重建干净数据。使用来自Biograph Vision Quadra PET/CT扫描仪的高质量数据训练一个三维卷积网络以生成得分函数，使模型能够捕捉从全身数据集中提取的准确PET分布信息。在来自四个扫描仪、四种示踪剂类型和六个剂量水平的数据集上对训练好的3D DDPM进行评估，这些数据集代表了广泛的临床场景。
所提出的3D DDPM始终优于二维DDPM、三维U-Net和三维生成对抗网络（3D GAN），表明其在所有测试条件下都具有卓越的去噪性能。此外，该模型的不确定性图显示出较低的方差，反映出其对输出结果有更高的置信度。
所提出的3D DDPM能够有效处理各种临床情况，包括剂量水平、扫描仪和示踪剂的变化，使其成为PET图像去噪的一个有前景的基础模型。本研究中训练好的3D DDPM模型可供研究人员现成使用，作为全身PET图像去噪解决方案。代码和模型可在https://github.com/Miche11eU/PET-Image-Denoising-Using-3D-Diffusion-Model获取。
