Despite recent developments in CT planning that enabled automation in patient positioning, time-consuming scout scans are still needed to compute dose profile and ensure the patient is properly positioned. In this paper, we present a novel method which eliminates the need for scout scans in CT lung cancer screening by estimating patient scan range, isocenter, and Water Equivalent Diameter (WED) from 3D camera images. We achieve this task by training an implicit generative model on over 60,000 CT scans and introduce a novel approach for updating the prediction using real-time scan data. We demonstrate the effectiveness of our method on a testing set of 110 pairs of depth data and CT scan, resulting in an average error of 5mm in estimating the isocenter, 13mm in determining the scan range, 10mm and 16mm in estimating the AP and lateral WED respectively. The relative WED error of our method is 4%, which is well within the International Electrotechnical Commission (IEC) acceptance criteria of 10%.

翻译：尽管近期CT规划技术的发展使得患者定位实现了自动化，但计算剂量分布并确保患者正确摆位仍需耗时进行定位扫描。本文提出了一种新颖方法，通过从3D相机图像中估计患者扫描范围、等中心和水等效直径（WED），消除CT肺癌筛查中的定位扫描需求。我们利用超过6万例CT扫描数据训练隐式生成模型完成此任务，并提出了一种利用实时扫描数据更新预测的创新方法。在包含110对深度数据与CT扫描的测试集上验证了方法有效性，结果显示：等中心估计平均误差为5mm，扫描范围确定误差为13mm，AP和侧向WED估计误差分别为10mm和16mm。本方法的相对WED误差为4%，完全满足国际电工委员会（IEC）10%的验收标准。