Performance comparison of quantitative metrics for analysis of in vivo Cherenkov imaging incident detection during radiotherapy

Objectives: Examine the responses of multiple image similarity metrics to detect patient positioning errors in radiotherapy observed through Cherenkov imaging, which may be used to optimize automated incident detection. Methods: An anthropomorphic phantom mimicking patient vasculature, a biological marker seen in Cherenkov images, was simulated for a breast radiotherapy treatment. The phantom was systematically shifted in each translational direction, and Cherenkov images were captured during treatment delivery at each step. The responses of mutual information (MI) and the. passing rate (%GP) were compared to that of existing field-shape matching image metrics, the Dice coefficient, and mean distance to conformity (MDC). Patient images containing other incidents were analyzed to verify the best detection algorithm for different incident types. Results: Positional shifts in all directions were registered by both MI and %GP, degrading monotonically as the shifts increased. Shifts in intensity, which may result from erythema or bolus-tissue air gaps, were detected most by %GP. However, neither metric detected beam-shape misalignment, such as that caused by dose to unintended areas, as well as currently employed metrics (Dice and MDC). Conclusions: This study indicates that different radiotherapy incidents may be detected by comparing both inter- and intrafractional Cherenkov images with a corresponding image similarity metric, varying with the type of incident. Future work will involve determining appropriate thresholds per metric for automatic flagging. Advances in knowledge: Classifying different algorithms for the detection of various radiotherapy incidents allows for the development of an automatic flagging system, eliminating the burden of manual review of Cherenkov images.

Automated summary

This study examines the responses of various image similarity metrics in detecting patient positioning errors in radiotherapy using Cherenkov imaging. The results show that different incidents can be detected by comparing Cherenkov images with image similarity metrics, providing a basis for the development of an automated flagging system.