用双提示词解决不同计数水平的PET去噪难题
Dual Prompting for Diverse Count-level PET Denoising
- 设计双提示机制，分别处理计数水平与通用去噪知识
- 在1940个低计数3D PET数据上显著提升去噪效果
- 适合医学影像去噪研究者及需要跨计数场景应用的场景
待去噪的正电子发射断层扫描（PET）图像具有多种计数水平，给统一模型处理带来挑战。本文借助蓬勃发展的提示学习，实现对不同计数水平的通用化PET去噪。提出双提示机制：显式计数水平提示提供具体先验信息，隐式通用去噪提示编码核心去噪知识。设计新颖的提示融合模块统一异构提示，并通过提示-特征交互模块将提示注入特征。提示可动态引导噪声条件下的去噪过程。因此，能高效训练统一去噪模型并部署于不同场景，仅需个性化提示。在97例¹⁸F-MK6240 Tau PET研究中，选取13-22%随机事件的1940个低计数3D PET体积进行评估，结果表明双提示方法在引入计数信息后性能显著提升，优于计数条件模型。
原文摘要 · Abstract (English)
The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this work, we resort to the recently flourished prompt learning to achieve generalizable PET denoising with different count levels. Specifically, we propose dual prompts to guide the PET denoising in a divide-and-conquer manner, i.e., an explicitly count-level prompt to provide the specific prior information and an implicitly general denoising prompt to encode the essential PET denoising knowledge. Then, a novel prompt fusion module is developed to unify the heterogeneous prompts, followed by a prompt-feature interaction module to inject prompts into the features. The prompts are able to dynamically guide the noise-conditioned denoising process. Therefore, we are able to efficiently train a unified denoising model for various count levels, and deploy it to different cases with personalized prompts. We evaluated on 1940 low-count PET 3D volumes with uniformly randomly selected 13-22\% fractions of events from 97 $^{18}$F-MK6240 tau PET studies. It shows our dual prompting can largely improve the performance with informed count-level and outperform the count-conditional model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。