Automated CT Lung Cancer Screening Workflow using 3D Camera
This work addresses a bottleneck in medical imaging workflows for healthcare providers by automating CT planning, though it is incremental as it builds on existing automation efforts.
The paper tackles the problem of eliminating time-consuming scout scans in CT lung cancer screening by estimating patient scan range, isocenter, and Water Equivalent Diameter from 3D camera images, achieving average errors of 5mm for isocenter, 13mm for scan range, and a relative WED error of 4%.
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%.