Purpose: Electronic portal image devices (EPIDs) have been investigated previously for beams-eye view (BEV) applications such as tumor tracking but are limited by low contrast-to-noise ratio (CNR) and detective quantum efficiency (DQE). We used a novel multilayer imager (MLI) with better performance, consisting of four stacked flat-panels, to measure improvements in fiducial tracking during liver stereotactic body radiation therapy (SBRT) procedures.
Methods: The prototype MLI was installed on a clinical TrueBeam linac in place of the conventional DMI single-layer EPID. The panel was extended during volumetric modulated arc therapy SBRT treatments in order to passively acquire data. A frame grabber card was connected to the imaging system. Images were acquired for six patients receiving SBRT to liver metastases over two fractions each, one with the MLI using all 4 layers and one with the MLI using the top layer only, representing a standard EPID. The acquired frames were processed by a previously published tracking algorithm modified to identify implanted radiopaque fiducials. Truth data was determined using respiratory traces combined with partial manual tracking. Results for 4- and 1-layer mode were compared against truth data for tracking accuracy and efficiency. Tracking and noise improvements as a function of gantry angle were determined.
Results: Tracking efficiency with 4-layers improved to 82.8% versus 58.4% for the 1-layer mode, a relative improvement of 41.7%. Fiducial tracking with 1-layer returned a root mean square error (RMSE) of 2.1mm compared to 4-layer RMSE of 1.5mm, a statistically significant (p<0.001) improvement of 0.6mm. The increase in successfully tracked frames correlated strongly with reduction in noise using 4-layer mode (r=0.913).
Conclusion: Increases in MV photon detection efficiency by utilization of a multi-layer imager (MLI) results in improved fiducial tracking for liver SBRT treatments. Future clinical applications utilizing BEV imaging may be enhanced by including similar noise reduction strategies.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by award number R01CA188446 from the National Institutes of Health.