论文标题
以多种低剂量PET图像（不同剂量水平）形式的先验知识是否可以改善标准剂量PET预测？
Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?
论文作者
论文摘要
减少注入剂量将导致宠物成像中的质量降解和信息丢失。为了解决这个问题，已经引入了深度学习方法，以预测相应的低剂量版本（L-PET）的标准PET图像（S-PET）。现有的基于深度学习的DeNoising方法仅依赖于单剂量的PET图像来预测S-PET图像。在这项工作中，我们提议以多种低剂量水平的PET图像（除目标低剂量水平）的形式利用先验知识，以估计S-PET图像。
Reducing the injected dose would result in quality degradation and loss of information in PET imaging. To address this issue, deep learning methods have been introduced to predict standard PET images (S-PET) from the corresponding low-dose versions (L-PET). The existing deep learning-based denoising methods solely rely on a single dose level of PET images to predict the S-PET images. In this work, we proposed to exploit the prior knowledge in the form of multiple low-dose levels of PET images (in addition to the target low-dose level) to estimate the S-PET images.
