A tool for patient-specific prediction of delivery discrepancies in machine parameters using trajectory log files.

Multileaf collimator (MLC) delivery discrepancy between planned and actual (delivered) positions have detrimental effect on the accuracy of dose distributions for both IMRT and VMAT. In this study, we evaluated the consistency of MLC delivery discrepancies over the course of treatment and over time to verify that a predictive machine learning model would be applicable throughout the course of treatment. Next, the MLC and gantry positions recorded in prior trajectory log files were analyzed to build a machine learning algorithm to predict MLC positional discrepancies during delivery for a new treatment plan. An open source tool was developed and released to predict the MLC positional discrepancies at treatment delivery for any given plan. Trajectory log files of 142 IMRT plans and 125 VMAT plans from 9 Varian TrueBeam linear accelerators were collected and analyzed. The consistency of delivery discrepancy over patient-specific quality assurance (QA) and patient treatment deliveries was evaluated. Data were binned by treatment site and machine type to determine their relationship with MLC and gantry angle discrepancies. Motion-related parameters including MLC velocity, MLC acceleration, control point, dose rate, and gravity vector, gantry velocity and gantry acceleration, where applicable, were analyzed to evaluate correlations with MLC and gantry discrepancies. Several regression models, such as simple/multiple linear regression, decision tree, and ensemble method (boosted tree and bagged tree model) were used to develop a machine learning algorithm to predict MLC discrepancy based on MLC motion parameters. MLC discrepancies at patient-specific QA differed from those at patient treatment deliveries by a small (mean = 0.0021 ± 0.0036 mm, P = 0.0089 for IMRT; mean = 0.0010 ± 0.0016 mm, P = 0.0003 for VMAT) but statistically significant amount, likely due to setting the gantry angle to zero for QA in IMRT. MLC motion parameters, MLC velocity and gravity vector, showed significant correlation (P < 0.001) with MLC discrepancy, especially MLC velocity, which had an approximately linear relationship (slope = -0.0027, P < 0.001, R2  = 0.79). Incorporating MLC motion parameters, the final generalized model trained by data from all linear accelerators can predict MLC discrepancy to a high degree of accuracy with high correlation (R2  = 0.86) between predicted and actual MLC discrepancies. The same prediction results were found across different treatment sites and linear accelerators. We have developed a machine learning model using trajectory log files to predict the MLC discrepancies during delivery. This model has been a released as a research tool in which a DICOM-RT with predicted MLC positions can be generated using the original DICOM-RT file as input. This tool can be used to simulate radiotherapy treatment delivery and may be useful for studies evaluating plan robustness and dosimetric uncertainties from treatment delivery.

Chuang KC ，Giles W ，Adamson J 《-》

On the use of trajectory log files for machine & patient specific QA.

Purpose:Trajectory log files are increasingly being utilized clinically for machine and patient specific QA. The process of converting the DICOM-RT plan to a deliverable trajectory by the linac control software introduces some uncertainty that is inherently incorporated into measurement-based patient specific QA but is not necessarily included for trajectory log file-based methods. Roughly half of prior studies have included this uncertainty in the analysis while the remaining studies have ignored it, and it has yet to be quantified in the literature.Methods:We collected DICOM-RT files from the treatment planning system and the trajectory log files from four TrueBeam linear accelerators for 25 IMRT and 10 VMAT plans. We quantified the DICOM-RT Conversion to Trajectory Residual (DCTR, difference between 'planned' MLC position from TPS DICOM-RT file and 'expected' MLC position (the deliverable MLC positions calculated by the linac control software) from trajectory log file) and compared it to the discrepancy between actual and expected machine parameters recorded in trajectory log files.Results:RMS of the DCTR was 0.0845 mm (range of RMS per field/arc: 0.0173-0.1825 mm) for 35 plans (114 fields/arcs) and was independent of treatment technique, with a maximum observed discrepancy at any control point of 0.7255 mm. DCTR was correlated with MLC velocity and was consistent over the course of treatment and over time, with a slight change in magnitude observed after a linac software upgrade. For comparison, the RMS of trajectory log file reported delivery error for moving MLCs was 0.0205 mm, thus DCTR is about four times the recorded delivery error in the trajectory log file.Conclusion:The uncertainty introduced from the conversion process by the linac control software from DICOM-RT plan to a deliverable trajectory is 3-4 times larger than the discrepancy between actual and expected machine parameters recorded in trajectory log files. This uncertainty should be incorporated into the analysis when using trajectory log file-based methods for analyzing MLC performance or patient-specific QA.

Chuang KC ，Giles W ，Adamson J 《Biomedical Physics &amp; Engineering Express》

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network.

Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accurate patient treatment. The deviations of individual leaf positions from its intended positions can lead to errors in the dose delivered to the patient and hence may adversely affect the treatment outcome. In this study, we propose a state-of-the-art machine learning (ML) method based on an artificial neural network (ANN) for accurately predicting the MLC leaf positional deviations during the dynamic IMRT treatment delivery priori using log file data. Data of ten patients treated with sliding window dynamic IMRT delivery were retrospectively retrieved from a single-institution database. The patients' plans were redelivered with no patient on the couch using a Varian linear accelerator equipped with a Millennium 120 HD MLC system. Then the machine recorded log files data, a total of over 400 files containing 360 800 control points, were collected. A total of 14 parameters were extracted from the planning data in the log files such as leaf planned positions, dose fraction, leaf velocity, leaf moving status, leaf gap, and others. Next, we developed a feed-forward ANN architecture mapping the input parameters with the output to predict the MLC leaf positional deviations during the delivery priori. The proposed model was trained on 70% of the total data using the delivered leaf positional data as a target response. The trained model was then validated and tested on 30% of the available data. The model accuracy was evaluated using the mean squared error (MSE), regression plot, and error histogram. The deviations between the individual MLC planned and delivered positions can reach up to a few millimeters, with a maximum deviation of 1.2 mm. The predicted leaf positions at control points closely matched the delivered positions for all MLC leaves during the treatment delivery. The ANN model achieved a maximum MSE of 0.0001 mm2 (root MSE of 0.0097 mm) in predicting the leaf positions at control points of test data for each leaf. The correlation coefficient, that measures the goodness of fit, was perfect (R = 0.999) in all plots indicating an excellent agreement between the predicted and delivered MLC positions for the training, validation, and test data. We successfully demonstrated a proposed ANN-based method capable of accurately predicting the individual MLC leaf positional deviations during the dynamic IMRT delivery priori. Our ML model based on ANN outperformed the reported accuracy in the literature of various ML models. The results of this study could be extended to actual application in the dose calculation/optimization, hence enhancing the gamma passing rate for patient-specific IMRT quality assurance.

Osman AFI ，Maalej NM ，Jayesh K 《-》

Commissioning and quality assurance for VMAT delivery systems: An efficient time-resolved system using real-time EPID imaging.

An ideal commissioning and quality assurance (QA) program for Volumetric Modulated Arc Therapy (VMAT) delivery systems should assess the performance of each individual dynamic component as a function of gantry angle. Procedures within such a program should also be time-efficient, independent of the delivery system and be sensitive to all types of errors. The purpose of this work is to develop a system for automated time-resolved commissioning and QA of VMAT control systems which meets these criteria. The procedures developed within this work rely solely on images obtained, using an electronic portal imaging device (EPID) without the presence of a phantom. During the delivery of specially designed VMAT test plans, EPID frames were acquired at 9.5 Hz, using a frame grabber. The set of test plans was developed to individually assess the performance of the dose delivery and multileaf collimator (MLC) control systems under varying levels of delivery complexities. An in-house software tool was developed to automatically extract features from the EPID images and evaluate the following characteristics as a function of gantry angle: dose delivery accuracy, dose rate constancy, beam profile constancy, gantry speed constancy, dynamic MLC positioning accuracy, MLC speed and acceleration constancy, and synchronization between gantry angle, MLC positioning and dose rate. Machine log files were also acquired during each delivery and subsequently compared to information extracted from EPID image frames. The largest difference between measured and planned dose at any gantry angle was 0.8% which correlated with rapid changes in dose rate and gantry speed. For all other test plans, the dose delivered was within 0.25% of the planned dose for all gantry angles. Profile constancy was not found to vary with gantry angle for tests where gantry speed and dose rate were constant, however, for tests with varying dose rate and gantry speed, segments with lower dose rate and higher gantry speed exhibited less profile stability. MLC positional accuracy was not observed to be dependent on the degree of interdigitation. MLC speed was measured for each individual leaf and slower leaf speeds were shown to be compensated for by lower dose rates. The test procedures were found to be sensitive to 1 mm systematic MLC errors, 1 mm random MLC errors, 0.4 mm MLC gap errors and synchronization errors between the MLC, dose rate and gantry angle controls systems of 1°. In general, parameters measured by both EPID and log files agreed with the plan, however, a greater average departure from the plan was evidenced by the EPID measurements. QA test plans and analysis methods have been developed to assess the performance of each dynamic component of VMAT deliveries individually and as a function of gantry angle. This methodology relies solely on time-resolved EPID imaging without the presence of a phantom and has been shown to be sensitive to a range of delivery errors. The procedures developed in this work are both comprehensive and time-efficient and can be used for streamlined commissioning and QA of VMAT delivery systems.

Zwan BJ ，Barnes MP ，Hindmarsh J ，Lim SB ，Lovelock DM ，Fuangrod T ，O'Connor DJ ，Keall PJ ，Greer PB ... -  《-》

RapidArc patient specific mechanical delivery accuracy under extreme mechanical limits using linac log files.

Wijesooriya K ，Aliotta E ，Benedict S ，Read P ，Rich T ，Larner J ... -  《MEDICAL PHYSICS》