An error detection method for real‐time EPID‐based treatment delivery quality assurance

Abstract

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, in...

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

“…where D a (v) stands for the v-th fold of the whole dataset at a-th run (26). At v-th (1 ≤ v ≤ V) trial, the v-th fold is pinched out as the test set, and the remained V − 1 folds are selected as the training set:…”

Strip detector array (SDA) for beam monitoring in radiotherapy: reconstruction of MLC parameters from multiple projections of flux

“…For high-throughput high-accuracy radiotherapy treatments continuous beam monitoring can become an important Quality Control (QC) tool not only for specific patient plans but also for linac Quality Assurance and linac maintenance [4]. In principle, monitoring of MLC can be achieved with detector arrays attached to the linac head [5][6][7] or mounted on a robotic arm (EPID) [8][9][10], or by using linac logfiles [11].…”

“…Such systems are required for future advancement of radiotherapy including automation of treatment without the need for pre-treatment patient specific IMRT QA [4]. Alternative detector principles for radiotherapy QA are under intense investigation, such as remote sensor detector arrays [12], topological detectors [14] or new pixelated detectors [15], as well as advanced evaluation of existing detectors such as for EPID [8][9][10][11] or IQM TM [6] which demonstrates the clinical necessity of a practical solution to real-time radiotherapy beam monitoring.…”

Dose estimation in patients treated with radiotherapy for SARS-COVID disease based on EPID measurement

“…The fluence map obtained with EPID was tested to measure the dose in real time [3][4][5], repeated treatment [6][7][8][9], point dose measurement [10] and dose distribution [11][12][13][14]. Fluence maps were also used to verify the correct operation of the MLC [15][16][17][18] or compatibility with the planned dose distribution [19]. EPID can be used as a dose meter in in vivo dosimetry [20,21].…”