Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Abstract

The purpose of this study was to develop a simple verification method for the routine quality assurance (QA) of Dynamic WaveArc (DWA) irradiation using electronic portal imaging device (EPID) images and log data analysis. First, an automatic calibration method utilizing the outermost multileaf collimator (MLC) slits was developed to correct the misalignment between the center of the EPID and the beam axis. Moreover, to verify the detection accuracy of the MLC position according to the EPID images, various positions of the MLC with intentional errors in the range 0.1–1 mm were assessed. Second, to validate the geometric accuracy during DWA irradiation, tests were designed in consideration of three indices. Test 1 evaluated the accuracy of the MLC position. Test 2 assessed dose output consistency with variable dose rate (160–400 MU/min), gantry speed (2.2–6°/s), and ring speed (0.5–2.7°/s). Test 3 validated dose output consistency with variable values of the above parameters plus MLC speed (1.6–4.2 cm/s). All tests were delivered to the EPID and compared with those obtained using a stationary radiation beam with a 0° gantry angle. Irradiation log data were recorded simultaneously. The 0.1‐mm intentional error on the MLC position could be detected by the EPID, which is smaller than the EPID pixel size. In Test 1, the MLC slit widths agreed within 0.20 mm of their exposed values. The averaged root‐mean‐square error (RMSE) of the dose outputs was less than 0.8% in Test 2 and Test 3. Using log data analysis in Test 3, the RMSE between the planned and recorded data was 0.1 mm, 0.12°, and 0.07° for the MLC position, gantry angle, and ring angle, respectively. The proposed method is useful for routine QA of the accuracy of DWA.

A novel quality assurance procedure for trajectory log validation using phantom‐less real‐time latency corrected EPID images

“…Because of their availability, correlation with the actual treatment delivery 1,2 and high precision, the use of trajectory log files is gaining popularity as a QA tool for patient specific and routine QA. [1][2][3][4][5][6][7] Note, however, that the parameters recorded are taken from the machine's control system; they are not measurements and can be in error. MLC positions reported in the log have been found to deviate from the actual delivered positions.…”

“…The machine state record includes parameters, such as energy, dose rate, position of each multileaf collimator (MLC) leaf, gantry angle, collimator angle, and couch position. Because of their availability, correlation with the actual treatment delivery 1,2 and high precision, the use of trajectory log files is gaining popularity as a QA tool for patient specific and routine QA 1–7 . Note, however, that the parameters recorded are taken from the machine's control system; they are not measurements and can be in error.…”

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files

“…Actual parameters such as MLC leaf position, MU, and gantry in delivery may deviate from planned parameters, which will cause dose differences. 14 – 16 Delivery fluence based on log files was used as the input for the prediction model, which is considered to be more accurate than the plan-based prediction model when taking into account the actual delivery parameters. Besides, the prediction model based on the log files can monitor the delivery accuracy continuously among different treatment fractions, while the plan-based prediction model is a one-time prediction.…”

Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation

“…Log file-based patient-specific QA usually involves the recalculation of the treatment plan with control points modified based upon log file data used as a representation of the "actual" delivery to be compared to the planned delivery. [23][24][25][26][27][28]32,[34][35][36][41][42][43][45][46][47][50][51][52]55 A number of authors have cautioned about the nonindependence of log files from the systems under investigation and potential insensitivity of log files to MLC mis-calibration or to faults in the MLC drive train and hence have suggested a need for separate MLC QA to assure log file accuracy. 20,22,23,32,37,38,43,45,50 In an attempt to validate log file-based QA systems, a number of authors have attempted to prove the sensitivity of log file-based QA to MLC position errors via a modification of treatment plan MLC positions to simulate leaf mispositioning.…”

“…20,22,23,32,37,38,43,45,50 In an attempt to validate log file-based QA systems, a number of authors have attempted to prove the sensitivity of log file-based QA to MLC position errors via a modification of treatment plan MLC positions to simulate leaf mispositioning. 32,41,42,48,50,55,56 However, it has been demonstrated via an electronic portal imaging device (EPID) imaging that log files can be insensitive to certain MLC positioning errors. 31,40,54 In the study of Agnew et al, 31 MLC position errors detected by the picket fence test were not evident in the log files.…”