用3D扩散模型+ControlNet实现自适应全身PET去噪，适配多种临床条件。
Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet
- 基于3D ControlNet架构，结合低剂量输入进行微调。
- 在临床数据集上优于现有方法，视觉与定量指标均更优。
- 可快速适配不同扫描仪、示踪剂和剂量的PET图像去噪。
正电子发射断层成像（PET）是临床诊断和基础研究中重要的影像技术，但受物理降质因素影响，存在分辨率低、信噪比差的问题。现有基于深度学习的去噪方法难以适应不同临床场景，如扫描仪类型、示踪剂选择、剂量水平和采集时间等差异。本文提出一种基于3D ControlNet的全身PET图像去噪新方法：首先使用大规模高质量正常剂量PET图像预训练3D去噪扩散概率模型（DDPM），随后在小规模配对的低剂量与正常剂量PET图像上微调，通过3D ControlNet引入低剂量输入，使模型可适应多种临床设置。基于临床PET数据集的实验表明，该框架在视觉质量与定量指标上均优于当前最先进方法。该即插即用方案支持大型扩散模型针对不同采集协议的PET图像进行高效微调与适配。
原文摘要 · Abstract (English)
Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influenced by factors such as scanner types, tracer choices, dose levels, and acquisition times. In this work, we proposed a novel 3D ControlNet-based denoising method for whole-body PET imaging. We first pre-trained a 3D Denoising Diffusion Probabilistic Model (DDPM) using a large dataset of high-quality normal-dose PET images. Following this, we fine-tuned the model on a smaller set of paired low- and normal-dose PET images, integrating low-dose inputs through a 3D ControlNet architecture, thereby making the model adaptable to denoising tasks in diverse clinical settings. Experimental results based on clinical PET datasets show that the proposed framework outperformed other state-of-the-art PET image denoising methods both in visual quality and quantitative metrics. This plug-and-play approach allows large diffusion models to be fine-tuned and adapted to PET images from diverse acquisition protocols.
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