An error detection method for real‐time EPID‐based treatment delivery quality assurance
Authors: Alves Victor Gabriel Leandro , Ahmed Mahmoud , Aliotta Eric , Choi Wookjin , Siebers Jeffrey Vincent 
Published: December 2020

Purpose

To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real‐time EPID images.

Methods

The on‐board EPID imager was used to measure cine‐EPID (~10 Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical plans. For each frame acquisition, planned apertures were interpolated from the treatment plan multileaf collimator (MLC) positions expected during the frame acquisition interval. Inaccurate deliveries were identified by monitoring in‐aperture missed fluence and out‐of‐aperture excess fluence beyond a specified buffer. Delivery errors were simulated by perturbing the planned MLC positions before comparison with nonperturbed measured apertures. Systematic 1–5 mm MLC leaf shifts were used to train a logistic regression model to determine the error detection threshold. Model accuracy was monitored using tenfold cross‐validation. The model’s error detection ability was tested with other error modes: plan control point (CP) weight perturbations, collimator rotations, random MLC leaf position errors, EPID imager shift, and stuck MLC leaf. The error detection accuracy was evaluated using the Matthews correlation coefficient (MCC) and the false positive rate (FPR). Per‐beam error thresholds of >1, >5, and >10% errant frames were tested to label per‐beam errors. The model also was tested for its ability to distinguish five cases with highly similar plans and compared with gamma analysis.

Results

Delivery errors were detected by monitoring intended per‐frame images with a 2 mm MLC buffer. Frame‐by‐frame aperture errors were identified with an optimal threshold of 0.3% of the expected aperture area. The per‐frame FPR was 0.02%. The MCC was 1.00 (perfect classification) for detection based on 1% of frames for random CP weight shift, 3 mm random MLC shifts, 90° and 180° collimator rotations, and an MLC leaf stuck after 10% of the beam delivery. The MCC for 2°, 4°, and 8° collimator rotation were 0.53, 0.76, and 0.96, respectively, for the 1% of beam delivery threshold. The 3 mm EPID shift had poor detection, with a minimum MCC of 0.14. The highly similar plans were reliably detected by the aperture check but were not detectable with gamma analysis.

Conclusion

The high error detection sensitivity and low FPR makes the aperture check error detection method well suited to pretreatment and during‐treatment beam delivery quality assurance (QA). The aperture check detects subtle beam delivery errors, including those resulting from MLC leaf positioning deviations, CP MU shifts, and stuck MLC leaves. Furthermore, the method can distinguish between highly similar treatment plans. Since the aperture check method monitors for the aperture shapes over a given MU interval, it is also sensitive to errors in MU per CP, without requiring dosimetric calibration of the EPID. The aperture check is one part of a Swiss cheese error detection scheme, which provides redundant error testing of multiple error modes, including nonaperture related errors. The rapid error detection, at 1% of a beam's delivery, make the aperture check a potential candidate for QA of on‐line adaptive radiotherapy, or other situations in which pretreatment delivery QA is impractical.