学习扫描：用于CT成像中个性化扫描的深度强化学习方法
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
摘要：
计算机断层成像（CT）对受试者进行X射线测量以重建断层图像。由于X射线具有放射性，因此出于安全考虑，需要控制X射线的总剂量。因此，我们只能选择有限数量的测量角度，并为每个角度分配有限的剂量。传统的方法，如压缩传感通常随机选择的角度和平均分配的允许剂量上。在大多数CT重建模型中，重点是设计有效的图像表示，而很少强调改善扫描策略。随机角度选择和等剂量分布的简单扫描策略通常表现良好，但对于每个个体受试者可能并不理想。更希望为每个受试者设计个性化的扫描策略，以获得更好的重建结果。在本文中，我们建议使用强化学习（RL）来学习个性化的扫描策略，以选择每个受试者的角度和每个角度的剂量。我们首先将CT扫描过程表示为MDP，然后使用现代深度RL方法来解决它。学习的个性化扫描策略不仅可以获得更好的重建结果，而且可以与不同的重建算法相结合。
英文摘要：
Computed Tomography (CT) takes X-ray measurements on the subjects to reconstruct tomographic images. As X-ray is radioactive, it is desirable to control the total amount of dose of X-ray for safety concerns. Therefore, we can only select a limited number of measurement angles and assign each of them limited amount of dose. Traditional methods such as compressed sensing usually randomly select the angles and equally distribute the allowed dose on them. In most CT reconstruction models, the emphasize is on designing effective image representations, while much less emphasize is on improving the scanning strategy. The simple scanning strategy of random angle selection and equal dose distribution performs well in general, but they may not be ideal for each individual subject. It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result. In this paper, we propose to use Reinforcement Learning (RL) to learn a personalized scanning policy to select the angles and the dose at each chosen angle for each individual subject. We first formulate the CT scanning process as an MDP, and then use modern deep RL methods to solve it. The learned personalized scanning strategy not only leads to better reconstruction results, but also shows strong generalization to be combined with different reconstruction algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
