Abstract Purpose: The purpose of this paper is to quantify if using an average intensity projection (AIP) scan or a 30% phase (mid-ventilation surrogate, MidV) scan as the reference image for patient position verification affects reproducibility of lung cancer patient alignment under free-breathing cone beam computed tomography (CBCT) image guidance and to analyze the effects of common clinical issues on registration variability. Methods: AIPs were retrospectively created for 16 lung patients (14 SBRT, 2 conventional treatments) originally planned/treated using the 30% phase MidV surrogate scan as reference. The study included 3-5 CBCTs from each patient. Registrations were performed between the AIP-CBCT and between the MidV-CBCT by 5 individuals (student, medical physics resident, medical resident, medical physicist, and attending physician) using MIM 6.2 image registration platform (Beachwood, OH). The images were rigidly registered, internal tumor volume (ITV) contours were displayed, and no rotational adjustments were allowed to reflect real treatment conditions. Additionally, the registrations for AIP-CBCT and MidV-CBCT were repeated 3 times by one individual for intra-observer variability assessment. Patient setup rotations, tumor volume, tumor motion, and breathing variability were estimated for correlation with registration variability. Results: The magnitude of the average intra-observer standard deviations from the lateral (LAT), anterior-posterior (AP), and superior-inferior (SI) directions for the AIP/CBCT and MidV/CBCT registrations were 0.9 mm and 1.2 mm, respectively. The magnitude of the average inter-observer standard deviations for the AIP/CBCT and MidV/CBCT were 1.7 mm and 1.8 mm, respectively. Average discrepancies over the whole population were found to be small; however, some individual patients presented high variability. Patient-specific cases with high variability were analyzed and observations on its potential causes are discussed. Conclusion: The differences in alignment using AIP versus MidV as the reference images are, when averaged over the population studied, very small and clinically irrelevant for PTV margins > 5mm; however, individual patients may be impacted in a clinically relevant manner if smaller margins, 3 mm and below, are used instead.

An approach to quantitatively determine patient surface contours as part of an augmented reality (AR) system for patient position and posture correction was developed. Quantitative evaluation of the accuracy of patient positioning and posture correction requires the knowledge of coordinates of the patient contour. The system developed uses the surface contours from the planning CT data as the reference surface coordinates. The corresponding reference point cloud is displayed on screen to enable AR assisted patient positioning. A 3D computer vision system using structured light then captures the current 3D surface of the patient. The offset between the acquired surface and the reference surface, representing the desired patient position, is the alignment error. Two codification strategies, spatial encoding, and temporal encoding, were examined. Spatial encoding methods require a single static pattern to work, thus enabling dynamic scenes to be captured. Temporal encoding methods require a set of patterns to be successively projected onto the object, the encoding for each pixel is only complete when the entire series of patterns has been projected. The system was tested on a camera tracking object. The structured light reconstruction was accurate to within ±1 mm, ±1.5 mm, and ±4 mm in x, y, and z-directions (camera optical axis) respectively. The method was integrated into a simplified AR system and a visualization scheme based on z-direction offset was developed. A demonstration of how the final AR-3D vision hybrid system can be used in a clinical situation was given using an anatomical teaching phantom. The system and visualisation worked well and demonstrated the proof of principal of the approach. It was found that the achieved accuracy was not yet sufficient for clinical use. Further work on improving the projector calibration accuracy is required. Both the camera registration process and 3D computer vision using structured light have been shown to be capable of sub-millimeter accuracy on their own. If that level of accuracy can be reproduced in this system, the concept presented can potentially be used in Oncology departments as a cost-effective patient setup guidance system for external beam radiotherapy, used in addition to current laser/portal imaging/cone beam CT based setup procedures.

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