  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%.
