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

Abstract

The use of trajectory log files for routine patient quality assurance is gaining acceptance. Such use requires the validation of the trajectory log itself. However, the accurate localization of a multileaf collimator (MLC) leaf while it is in motion remains a challenging task. We propose an efficient phantom-less technique using the EPID to verify the dynamic MLC positions with high accuracy. Measurements were made on four Varian TrueBeams equipped with M120 MLCs. Two machines were equipped with the S1000 EPID; two were equipped with the S1200 EPID. All EPIDs were geometrically corrected prior to measurements. Dosimetry mode EPID measurements were captured by a frame grabber card directly linked to the linac. All leaf position measurements were corrected both temporally and geometrically. The readout latency of each panel, as a function of pixel row, was determined using a 40 × 1.0 cm 2 sliding window (SW) field moving at 2.5 cm/s orthogonal to the row readout direction. The latency of each panel type was determined by averaging the results of two panels of the same type. Geometric correction was achieved by computing leaf positions with respect to the projected isocenter position as a function of gantry angle. This was determined by averaging the central axis position of fields at two collimator positions of 90°and 270°. The radiological to physical leaf end position was determined by comparison of the measured gap with that determined using a feeler gauge. The radiological to physical leaf position difference was found to be 0.1 mm. With geometric and latency correction, the proposed method was found to be improve the ability to detect dynamic MLC positions from 1.0 to 0.2 mm for all leaves. Latency and panel residual geometric error correction improve EPID-based MLC position measurement. These improvements provide for the first time a trajectory log QA procedure.

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

“…Although EPID imaging has its own inherent inaccuracies and uncertainties, it is at least independent of the MLC, gantry and dose control systems used to modulate a VMAT delivery if not from the linac itself, whereas log files are not independent of the dynamic systems under investigation. Cine‐EPID imaging has been demonstrated as a data source for plan recalculation to allow DVH analysis of actual plan deliveries, 59 and it has also been recently claimed that the process has been developed to the required accuracy that it can be used to validate log files 54 . If this level of accuracy is confirmed then cine‐EPID could be considered to be a superior patient‐specific QA data source to log files as it is free of MLC drive train error insensitivity and is independent of the systems under investigation while maintaining the level of required accuracy and convenience.…”

“…These findings are in agreement with and build upon those from previous studies. 31,40,54 Such insensitivity creates a significant risk of false indication of acceptable plan deliverability results from log file-based patient-specific QA methods.…”

“…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. The errors were resolved when the MLC leaf motor or Tnut was replaced.…”

“…The MLC log file data is generated from the MLC controller, which records the MLC motor counts and then calculates leaf position by application of a conversion factor 19 . Due to their convenience and high temporal resolution, the use of machine log files as input data for linac and patient‐specific QA has been heavily investigated 19–55 . 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 23–28,32,34–36,41–43,45–47,50–52,55 .…”

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].…”