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.

翻译：由于X光是放射性的,因此为了安全考虑,最好控制X光的剂量总量。因此,我们只能选择有限的测量角度,并分配有限的剂量。传统方法,例如压缩遥感通常随机地选择角度,并平均分配允许剂量。在大多数CT重建模型中,重点是设计有效的图像显示,而较少强调的是改进扫描战略。随机角度选择和同等剂量分布的简单扫描战略一般运作良好,但每个对象可能并不理想。为每个对象设计个性化扫描战略以获得更好的重建结果更为可取。在本文件中,我们提议使用“加强学习”等传统方法学习个人化扫描政策,以选择角度和每个对象选择的剂量。我们首先将CT扫描进程设计成一个MDP,然后使用现代深度RL方法来解决这个问题。学习的个人化扫描战略不仅能够带来更好的重建结果,而且还能显示与更强的重建结果相结合。