用文本控制的AI方法，能自动去噪不同剂量的PET医学影像。
Text controllable PET denoising
- 结合CLIP视觉语言模型与U-Net，实现文本引导的PET图像去噪。
- 在多种计数水平下均显著提升图像质量，定量指标明显改善。
- 适合临床医生快速获取清晰影像，或缩短扫描时间以降低辐射。
正电子发射断层扫描（PET）是医学诊断中的关键工具，可揭示人体内的分子过程。然而，PET图像常因扫描设备、重建算法、示踪剂特性、剂量/计数水平和采集时间等因素导致复杂噪声，影响诊断。本文提出一种新型文本引导去噪方法，可在单一模型中适应多种计数水平的PET图像增强。该模型融合预训练的CLIP模型特征与基于U-Net的去噪结构。实验结果表明，所提方法在定性和定量评估中均有显著提升，展现出良好的灵活性，具备应对复杂去噪需求或缩短采集时间的潜力。
原文摘要 · Abstract (English)
Positron Emission Tomography (PET) imaging is a vital tool in medical diagnostics, offering detailed insights into molecular processes within the human body. However, PET images often suffer from complicated noise, which can obscure critical diagnostic information. The quality of the PET image is impacted by various factors including scanner hardware, image reconstruction, tracer properties, dose/count level, and acquisition time. In this study, we propose a novel text-guided denoising method capable of enhancing PET images across a wide range of count levels within a single model. The model utilized the features from a pretrained CLIP model with a U-Net based denoising model. Experimental results demonstrate that the proposed model leads significant improvements in both qualitative and quantitative assessments. The flexibility of the model shows the potential for helping more complicated denoising demands or reducing the acquisition time.
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