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%.

通过训练隐式生成模型并引入实时扫描数据更新预测的新方法，我们在不需要斜视扫描的情况下，通过从3D相机图像中估计患者扫描范围、等轴中心和水当量直径，为CT肺癌筛查提供了一种新颖的方法。在测试集的110对深度数据和CT扫描上展示了我们方法的有效性，得出了等轴中心估计误差平均为5mm，扫描范围测定误差为13mm，前后方向水当量直径估计误差为10mm和16mm。我们方法的相对水当量直径误差为4%，远远在国际电工委员会（IEC）接受标准的10%之内。

使用3D相机的自动化CT肺癌筛查工作流程