A feasibility study of markerless fluoroscopic gating for lung cancer radiotherapy using 4DCT templates.

A major difficulty in conformal lung cancer radiotherapy is respiratory organ motion, which may cause clinically significant targeting errors. Respiratory-gated radiotherapy allows for more precise delivery of prescribed radiation dose to the tumor, while minimizing normal tissue complications. Gating based on external surrogates is limited by its lack of accuracy, while gating based on implanted fiducial markers is limited primarily by the risk of pneumothorax due to marker implantation. Techniques for fluoroscopic gating without implanted fiducial markers (markerless gating) have been developed. These techniques usually require a training fluoroscopic image dataset with marked tumor positions in the images, which limits their clinical implementation. To remove this requirement, this study presents a markerless fluoroscopic gating algorithm based on 4DCT templates. To generate gating signals, we explored the application of three similarity measures or scores between fluoroscopic images and the reference 4DCT template: un-normalized cross-correlation (CC), normalized cross-correlation (NCC) and normalized mutual information (NMI), as well as average intensity (AI) of the region of interest (ROI) in the fluoroscopic images. Performance was evaluated using fluoroscopic and 4DCT data from three lung cancer patients. On average, gating based on CC achieves the highest treatment accuracy given the same efficiency, with a high target coverage (average between 91.9% and 98.6%) for a wide range of nominal duty cycles (20-50%). AI works well for two patients out of three, but failed for the third patient due to interference from the heart. Gating based on NCC and NMI usually failed below 50% nominal duty cycle. Based on this preliminary study with three patients, we found that the proposed CC-based gating algorithm can generate accurate and robust gating signals when using 4DCT reference template. However, this observation is based on results obtained from a very limited dataset, and further investigation on a larger patient population has to be done before its clinical implementation.