Patient‐specific quality assurance using machine log files analysis for stereotactic body radiation therapy (SBRT)

Abstract

An in-house trajectory log analysis program (LOGQA) was developed to evaluate the delivery accuracy of volumetric-modulated arc therapy (VMAT) for stereotactic body radiation therapy (SBRT). Methods have been established in LOGQA to provide analysis on dose indices, gantry angles, and multi-leaf collimator (MLC) positions. Between March 2019 and May 2020, 120 VMAT SBRT plans of various treatment sites using flattening filter-free (FFF) mode were evaluated using both LOGQA and phantom measurements. Gantry angles, dose indices, and MLC positions were extracted from log and compared with each plan. Integrated transient fluence map (ITFM) was reconstructed from log to examine the deviation of delivered fluence against the planned one. Average correlation coefficient of dose index versus gantry angle and ITFM for all patients were 1.0000, indicating that the delivered beam parameters were in good agreement with planned values. Maximum deviation of gantry angles and monitor units (MU) of all patients were less than 0.2 degree and 0.03 % respectively. Regarding MLC positions, maximum and rootmean-square (RMS) deviations from planned values were less than 0.6 mm and 0.3 mm respectively, indicating that MLC positions during delivery followed planned values in precise manner. Results of LOGQA were consistent with measurement, where all gamma-index passing rates were larger than 95 %, with 2 %/2 mm criteria. Three types of intentional errors were introduced to patient plan for software validation. LOGQA was found to recognize the introduced errors of MLC positions, gantry angles, and dose indices with magnitudes of 1 mm, 1 degree, and 5 %, respectively, which were masked in phantom measurement. LOGQA was demonstrated to have the potential to reduce or even replace patient-specific QA measurements for SBRT plan delivery provided that the frequency and amount of measurement-based machine-specific QA can be increased to ensure the log files record real values of machine parameters.

An investigation of using log‐file analysis for automated patient‐specific quality assurance in MRgRT

“…This combination is particularly essential in meeting the resource intensive online ART program. Although our study found that the overall quality assurance of γ LV has significant correlation with γ AC, complement the other published works, 5,17 certain discrepancies between γ AC and γ LV were observed which were attributed to unaccounted factors in the log‐file fluence analysis, such as the couch transmission, measurement setup, and treatment planning system accuracy. For example, we found that in 1.5% of all the fields, γ AC was significantly lower than γ LV .…”

“…During this study, we noticed the log‐file reported MLC positions significantly different, as much as 30 mm, from the plan positions which could amount to an order of magnitude larger than our institution experience 16 and other works. 3 , 6 , 17 , 18 After some investigations, it was found that the MLCs, outside the guard leaves or under the diaphragms, often had more than 1.0 mm discrepancy. Figure 4 shows an example of MLC leaf deviation of more than 30 mm (in red circle).…”

Comparison of dosimetric effects of MLC positional errors on VMAT and IMRT plans for SBRT radiotherapy in non-small cell lung cancer

“…ICRU 142 suggested an action level of 1 mm gap width deviation [ 16 ]. Chow et al examined the VAMT plan for SBRT using log files, and found that the maximum MLC positional error was 0.6 mm [ 17 ]. Oliver et al analyzed the effect of MLC positioning errors for VMAT plans and concluded that the MLC positioning errors for VMAT treatments should be within 0.6 mm to keep the dose variation in the target coverage within 2% [ 18 ].…”

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

“…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.…”

“…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.…”