Automated CT Lung Cancer Screening Workflow using 3D Camera

TL;DR

This paper introduces a novel method for CT lung cancer screening that uses 3D camera images to eliminate scout scans, accurately estimating patient positioning parameters with minimal error, thus streamlining the screening process.

Contribution

The paper presents a new approach that estimates patient scan range, isocenter, and WED from 3D camera images, trained on over 60,000 CT scans, reducing the need for scout scans in lung cancer screening.

Findings

Average isocenter error of 5mm

Scan range estimation error of 13mm

WED estimation errors within IEC criteria

Abstract

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%.