Multileaf Collimator Position Errors Research Articles

Characterization of positional accuracy of MLC using an ionization chamber array

PurposeThe purpose of this study is to characterize the positional accuracy of the multileaf collimator (MLC) using an ionization chamber array and develop an MLC quality assurance (QA) method. MethodsHalf of the volume for each ionization chamber located at the lower layer of the device was occluded by a specific leaf using the half-beam block (HBB) method, and the corresponding ionization chamber response was used as a baseline for the subsequent MLC QA study. The sensitivity of the method was assessed by introducing known leaf positional errors, varying from 0.5 to 2.5 mm in 0.5 mm increments in the positive and negative directions, into the baseline. The relative outputs of the detectors were linearly corrected according to the leaf position error recorded. The relationship between detector response variation relative to the baseline and the introduced leaf error was determined by function. The usefulness of the MLC QA technique developed in this study was verified weekly over 5 months. ResultsDuring the development of the algorithm, the maximum leaf absolute random position error was 0.3 mm. A strong linear relationship was observed between the relative output of the detector and the MLC positional error introduced, and the slope and coefficient of determination were −0.05159 and 0.99928, respectively. The mean MLC positional accuracy across all leaves for the error field and HBB baseline field over the 5-month assessment was 0.0649 ± 0.1340 mm and 0.0002± 0.0835 mm, respectively. ConclusionsThe automated MLC QA procedure we proposed has proven to be effective and robust, and satisfies the American Association of Physicists in Medicine (AAPM) Task Group Report 198 guideline criteria of ± 1 mm.

Application of error classification model using indices based on dose distribution for characteristics evaluation of multileaf collimator position errors

This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose–volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as 'error-free' or 'any-error.' For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.

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

PurposeMulti‐leaf‐collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is to test the sensitivity of machine log files in comparison to electronic portal imaging device (EPID)‐based measurements to MLC position errors caused by leaf backlash. The secondary aim is to investigate the effect of MLC leaf backlash on MLC leaf motion during clinical dynamic plan delivery.MethodsThe sensitivity of machine log files and two EPID‐based measurements were assessed via a controlled experiment, whereby the length of the “T” section of a series of 12 MLC leaf T‐nuts in a Varian Millennium MLC for a Trilogy C‐series type linac was reduced by sandpapering the top of the “T” to introduce backlash. The built‐in machine MLC leaf backlash test as well as measurements for two EPID‐based dynamic MLC positional tests along with log files were recorded pre‐ and post‐T‐nut modification. All methods were investigated for sensitivity to the T‐nut change by assessing the effect on measured MLC leaf positions. A reduced version of the experiment was repeated on a TrueBeam type linac with Millennium MLC.ResultsNo significant differences before and after T‐nut modification were detected in any of the log file data. Both EPID methods demonstrated sensitivity to the introduced change at approximately the expected magnitude with a strong dependence observed with gantry angle. EPID‐based data showed MLC positional error in agreement with the micrometer measured T‐nut length change to 0.07 ± 0.05 mm (1 SD) using the departmental routine QA test. Backlash results were consistent between linac types.ConclusionMachine log files appear insensitive to MLC position errors caused by MLC leaf backlash introduced via the T‐nut. The effect of backlash on clinical MLC motions is heavily gantry angle dependent.

Evaluation of the ability of three commercially available dosimeters to detect systematic delivery errors in step-and-shoot IMRT plans.

There is limited data on error detectability for step-and-shoot intensity modulated radiotherapy (sIMRT) plans, despite significant work on dynamic methods. However, sIMRT treatments have an ongoing role in clinical practice. This study aimed to evaluate variations in the sensitivity of three patient-specific quality assurance (QA) devices to systematic delivery errors in sIMRT plans. Four clinical sIMRT plans (prostate and head and neck) were edited to introduce errors in: Multi-Leaf Collimator (MLC) position (increasing field size, leaf pairs offset (1-3 mm) in opposite directions; and field shift, all leaves offset (1-3 mm) in one direction); collimator rotation (1-3 degrees) and gantry rotation (0.5-2 degrees). The total dose for each plan was measured using an ArcCHECK diode array. Each field, excluding those with gantry offsets, was also measured using an Electronic Portal Imager and a MatriXX Evolution 2D ionisation chamber array. 132 plans (858 fields) were delivered, producing 572 measured dose distributions. Measured doses were compared to calculated doses for the no-error plan using Gamma analysis with 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria (1716 analyses). Generally, pass rates decreased with increasing errors and/or stricter gamma criteria. Pass rate variations with detector and plan type were also observed. For a 3%/3 mm gamma criteria, none of the devices could reliably detect 1 mm MLC position errors or 1 degree collimator rotation errors. This work has highlighted the need to adapt QA based on treatment plan type and the need for detector specific assessment criteria to detect clinically significant errors.

Error detection model developed using a multi‐task convolutional neural network in patient‐specific quality assurance for volumetric‐modulated arc therapy

In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA. A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1mm in lateral, longitudinal, and vertical directions). The error-introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in-house software. Those 13 types of dose difference maps, consisting of an error-free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi-task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error-free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2mm and 2%/1mm (dose difference/distance to agreement). For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2mm, and gamma analysis with the criteria set to 2%/1mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.

Usability of detecting delivery errors during treatment of prostate VMAT with a gantry-mounted transmission detector.

Volumetric‐modulated arc therapy (VMAT) requires highly accurate control of multileaf collimator (MLC) movement, rotation speed of linear accelerator gantry, and monitor units during irradiation. Pretreatment validation and monitoring of these factors during irradiation are necessary for appropriate VMAT treatment. Recently, a gantry mounted transmission detector “Delta4 Discover® (D4D)” was developed to detect errors in delivering doses and dose distribution immediately after treatment. In this study, the performance of D4D was evaluated. Simulation plans, in which the MLC position was displaced by 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm from the clinically used original plans, were created for ten patients who received VMAT treatment for prostate cancer. Dose deviation (DD), distance‐to‐agreement (DTA), and gamma index analysis (GA) for each plan were evaluated by D4D. These results were compared to the results (DD, DTA and GA) measured by Delta4 Phantom + (D4P). We compared the deviations between the planned and measured values of the MLC stop positions A‐side and B‐side in five clinical cases of prostate VMAT during treatment and measured the GA values. For D4D, when the acceptable errors for DD, DTA, and GA were determined to be ≤3%, ≤2 mm, and ≤3%/2 mm, respectively, the minimum detectable errors in the MLC position were 2.0, 1.5, and 1.5 mm based on DD, DTA, and GA respectively. The corresponding minimum detectable MLC position errors were 2.0, 1.0, and 1.5 mm, respectively, for D4P. The deviation between the planned and measured position of MLC stopping point of prostate VMAT during treatment was stable at an average of −0.09 ± 0.05 mm, and all GA values were above 99.86%. In terms of delivering doses and dose distribution of VMAT, error detectability of D4D was comparable to that of D4P. The transmission‐type detector “D4D” is thus suitable for detecting delivery errors during irradiation.

Comparison of pretreatment VMAT quality assurance with the integral quality monitor (IQM) and electronic portal imaging device (EPID).

The purpose of this study was to compare pretreatment volumetric modulated arc therapy (VMAT) quality assurance (QA) measurements and evaluate the multileaf collimator (MLC) error sensitivity of two detectors: the integral quality monitor (IQM) system (iRT systems IQM) and the electronic portal imaging device (EPID) (Varian PortalVision aS1200). Pretreatment QA measurements were performed for 20 retrospective VMAT plans (53 arcs). A subset of ten plans (23 arcs) was used to investigate MLC error sensitivity of each device. Eight MLC error plans were created for each VMAT plan. The errors included systematic opening/closing (±0.25, ±0.50, ±0.75 mm) of the MLC and random positional errors (1 mm) for individual/groups of leaves. The IQM was evaluated using the percent error of the measured cumulative signal relative to the calculated signal. The EPID was evaluated using two methods: a novel percent error of the measured relative to the predicted cumulative signals, and gamma (γ) analysis (1%/1 mm, 2%/2 mm, 3%/3 mm and 3%/1 mm for Stereotactic Body Radiation Therapy plans). The average change in maximum dose obtained from dose‐volume histogram (DVH) data and change in detector signals for different systematic MLC shifts was also compared. Cumulative signal differences showed similar levels of agreement between measured and expected detector signals (IQM: 1.00 ± 0.55%; EPID: 1.22 ± 0.92%). Results from γ analysis lacked specificity. Only the 1%/1 mm criteria produced data with remarkable differences. A strong linear correlation was observed between IQM and EPID cumulative signal differences with MLC error magnitude (R = 0.99). Likewise, results indicate a strong correlation between the cumulative signal for both detectors and DVH dose (RIQM = 0.99; REPID = 0.97). In conclusion, use of cumulative signal differences could be more useful for detecting errors in treatment delivery in EPID than γ analysis.

Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters [i.e., MLC transmission factor (TF) and dosimetric leaf gap (DLG)] and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms [decision tree, k-nearest neighbor (kNN), support vector machine (SVM), logistic regression, and random forest] for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform fourfold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 and 2%/2mm for all of the types of error. The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM, and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM, and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression, and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.

Trajectory log analysis and cone-beam CT-based daily dose calculation to investigate the dosimetric accuracy of intensity-modulated radiotherapy for gynecologic cancer.

This study evaluated unexpected dosimetric errors caused by machine control accuracy, patient setup errors, and patient weight changes/internal organ deformations. Trajectory log files for 13 gynecologic plans with seven‐ or nine‐beam dynamic multileaf collimator (MLC) intensity‐modulated radiation therapy (IMRT), and differences between expected and actual MLC positions and MUs were evaluated. Effects of patient setup errors on dosimetry were estimated by in‐house software. To simulate residual patient setup errors after image‐guided patient repositioning, planned dose distributions were recalculated (blurred dose) after the positions were randomly moved in three dimensions 0–2 mm (translation) and 0°–2° (rotation) 28 times per patient. Differences between planned and blurred doses in the clinical target volume (CTV) D98% and D2% were evaluated. Daily delivered doses were calculated from cone‐beam computed tomography by the Hounsfield unit‐to‐density conversion method. Fractional and accumulated dose differences between original plans and actual delivery were evaluated by CTV D98% and D2%. The significance of accumulated doses was tested by the paired t test. Trajectory log file analysis showed that MLC positional errors were −0.01 ± 0.02 mm and MU delivery errors were 0.10 ± 0.10 MU. Differences in CTV D98% and D2% were <0.5% for simulated patient setup errors. Differences in CTV D98% and D2% were 2.4% or less between the fractional planned and delivered doses, but were 1.7% or less for the accumulated dose. Dosimetric errors were primarily caused by patient weight changes and internal organ deformation in gynecologic radiation therapy.

Correlation Between Average Segment Width and Gamma Passing Rate as a Function of MLC Position Error in Volumetric Modulated Arc Therapy.

Objective:This study analyzed the correlation between the average segment width (ASW) and gamma passing rate according to the multi-leaf collimator (MLC) position error.Method:To evaluate the changes in the gamma passing rate according to the MLC position error, 21 volumetric modulated arc therapy (VMAT) plans were generated using pelvic lymph node metastatic prostate cancer patient's data which is sensitive to MLC position errors as they involve several long, narrow, irregular fields. The ASW for each VMAT plan was calculated using our own code developed using Visual Basic for Applications (VBA). The gamma passing rate of the VMAT plan according to the MLC position error was evaluated using ArcCHECK (Sun Nuclear, Melbourne, FL, USA) while inducing symmetric MLC position errors in 0.25 mm intervals from −1 mm to +1 mm in the infinity medical linear accelerator (Elekta AB, Stockholm, Sweden). Finally, we examined the correlation between the change in the passing rate () due to the MLC position error and the ASW in VMAT through linear regression analysis using the least squares method.Results:The ASW and were found to have a linear correlation according to the MLC position error, and the coefficient of determination was 0.88. For a 1 mm position error of MLC in VMAT, the gamma passing rate improved by approximately 11.9% as the ASW increased by 10 mm.Conclusion:These results are expected to be employed as guidelines to minimize the dose uncertainty due to MLC position error in VMAT.

Determination of Multileaf Collimator Positional Errors as a Function of Dose Rate, Speed, and Delivery Interruption for Volumetric-Modulated Arc Therapy Delivery.

Aim:To determine the multileaf collimator positional error (MLC-PE) during volumetric modulated arc therapy (VMAT) delivery by studying the time-dependent MLC velocity in mathematically derivable trajectories such as straight line and conic sections.Materials and Methods:VMAT delivery is planned in a way that MLCs are moving in a locus which can be defined by mathematical functions such as linear, parabolic, or circular velocity (PV or CV). The VMAT delivery was interrupted either once or multiple times during the delivery and projection images of the same were acquired in electronic portal imaging device. MLC-PE was then analyzed as a function of dose rate (DR), and MLC speed (SP) and number of interruptions in treatment delivery. In VMAT delivery with linear MLC motion, the delivery was interrupted either once (linear motion single interruption) or multiple (three) times (linear motion multiple interruptions). For PV and CV MLC velocity, the MLC motions are interrupted multiple times.Results:The maximum individual error obtained (DR of 35 MU/min, SP of 2.0 cm/s) was 1.96 ± 0.1 mm. Only 4.4% of MLCs showed ≥ ±1 mm positional error. When the treatment delivery is interrupted multiple times in VMAT delivery, the influence of interruption in MLC-PE overwhelmed the influence by DR and SP. For a sub-group analysis of independent and dependent variables, the mean MLC-PE was 0.18 ± 0.4 mm 0.19 ± 0.42 mm, respectively.Conclusion:Determination of MLC-PEs using a mathematical function without approximation indicates that MLC-PE is not a function of MLC speed. In less than 5% of the studied scenarios, the MLC-PE exceeds its tolerance value (±1 mm). The MLC-PE is significantly less in modern machines due to advancements in the delivery mechanism.

Effect of MU-weighted multi-leaf collimator position error on dose distribution of SBRT radiotherapy in peripheral non-small cell lung cancer.

PurposePosition accuracy of the multi‐leaf collimator (MLC) is essential in stereotactic body radiotherapy (SBRT). This study is aimed to investigate the dosimetric impacts of the MU‐weighted MLC positioning uncertainties of SBRT for patients with early stage peripheral non‐small cell lung cancer (NSCLC).MethodsThree types of MLC position error were simulated: Type 1, random error; Type 2, system shift, in which both MLC banks shifted to the left or right direction; and Type 3, in which both MLC banks moved with same magnitudes in the opposite directions. Two baseline plans were generated: an automatic plan (AP) and a manually optimized plan (MP). Multi‐leaf collimator position errors were introduced to generate simulated plans with the preset MLC leaf position errors, which were then reimported into the Pinnacle system to generate simulated plans, respectively. The dosimetric parameters (CI, nCI, GI, etc.) and gEUD values of PTV and OARs were calculated. Linear regression between MU‐weighted/unweighted MLC position error and gEUD was performed to obtain dose sensitivity.ResultsThe dose sensitivities of the PTVs were −4.93, −38.94, −41.70, −55.55, and 30.33 Gy/mm for random, left shift, right shift, system close, and system open MLC errors, respectively. There were significant differences between the MU‐weighted and the unweighted dose sensitivity, which was −38.94 Gy/mm vs −3.42 Gy/mm (left shift), −41.70 Gy/mm vs −3.56 Gy/mm (right shift), −55.55 Gy/mm vs −4.84 Gy/mm (system close), and 30.33 vs 2.64 Gy/mm (system open). For the system open/close MLC errors, as the PTV volume became larger, the dose sensitivity decreased. APs provided smaller dose sensitivity for the system shift and system close MLC errors compared to the conventional MPs.ConclusionsThere was significant difference in dose sensitivity between MU‐weighted and unweighted MLC position error of SBRT radiotherapy in peripheral NSCLC. MU is suggested to be included in the dosimetric evaluation of the MLC misalignments, since it is much closer to clinical radiotherapy.

Characterization of positional accuracy of a double-focused and double-stack multileaf collimator on an MR-guided radiotherapy (MRgRT) Linac using an IC-profiler array.

With advance magnetic resonance (MR)-guided online adaptive radiotherapy (MRgoART) relying on calculation-based intensity-modulated radiation therapy (IMRT) quality assurance (QA), accurate and sensitive QA of the multileaf collimator (MLC) becomes an increasingly essential component for routine machine QA. As such, it is important to assure compliance with the AAPM TG142 guidelines to supplement calculation-based QA methods for an online adaptive radiotherapy program. We have developed and implemented an efficient and highly sensitive QA procedure using an ionization chamber profiler (ICP) array to enable real-time characterization of the positional accuracy of a double-focused and double-stacked MLC on a clinical MR-guided radiotherapy (MRgRT) system and to supplement calculation-based QA for an MRgoART program. An in-house MR-compatible jig was used to position the ICP (detector resolution 5mm on X/Y axis) at an extended SDD of 108.4cm to enable each MLC leaf (8.3mm leaf width at isocenter) to be uniquely determined by two neighboring ion chambers. The MRgRT linac system utilizes a novel jawless, double-focused, and double-stacked MLC design such that the upper bank (MLC1) and lower bank (MLC2) are offset by half a leaf width. Positional accuracy was characterized by three methods: single bank half-beam block (HBB) at central axis, forward slash diagonal (FSD), and backslash diagonal (BSD) at off-axis. Measurements were performed for each bank in which each leaf occludes half of a detector. A corresponding reference field with the MLC retracted from occlusion was measured. The sensitivities of HBB, FSD, and BSD were evaluated by introducing 0.5-2.5mm of known errors in 0.5mm increments, in both positive and negative directions. The relationship between detector response and MLC error was established. Over a 6-month longitudinal assessment, we have evaluated MLC performance with weekly QA of HBB among cardinal angles, and monthly QA of FSD and BSD. A strong correlation was found between detector response of percentage dose difference and MLC positional error introduced (N=350 introduced errors) for both HBB and FSD/BSD with coefficient of determination of 0.999 and 0.977, respectively. The relationship between detector response to MLC positional change was found to be 20.65%/mm for HBB and 11.14%/mm for FSD and BSD. At baseline, the mean MLC positional accuracy averaged across all leaves was 0.06±0.27mm (HBB), 0.04±0.52mm (FSD), -0.06±0.51mm (BSD). The mean MLC positional accuracy relative to baseline over the 6-month assessment was found to be highly reproducible at 0.00±0.12mm (HBB; N=28weeks), -0.02±0.19mm (FSD; N=6months), -0.03±0.19mm (BSD; N=6months). Positional accuracy of a novel jawless, double-focused, double-stacked MLC has been characterized and monitored over 6 months with an efficient, highly sensitive, and robust method using an ICP array. This routine QA method supplements calculation-based IMRT QA for an online adaptive radiotherapy program. Longitudinal assessment demonstrated no-drift in the MLC calibration. A highly reproducible jig setup allowed the validation of MLC positional accuracy to be within TG142 criteria of ±1mm for 99% of measurements (i.e., 100% HBB, 95% BSD, 95% FSD) over the 6-month assessment.

Picket fence test of Agility MLCs using linac log data

The multileaf collimator (MLC) performance is critical during intensity modulated radiotherapy (IMRT) delivery. Picket fence test is typically performed by acquiring images of MLC gaps using film or electronic portal imaging device (EPID) to assess the MLC positional accuracy as part of a comprehensive weekly quality assurance (QA) programme. This study aims to investigate the accuracy of MLC position of Agility MLCs (Elekta, Stockholm, Sweden) using linac log data. The positions of MLCs were tracked during picket fence test using Elekta’s propriety optical tracking system on the linac head. The data were recorded at four cardinal gantry angles over six weeks. The fluence from the picket fence was constructed using the tracked MLC positions and compared with the fluence obtained from EPID. The fluence of the picket fence pattern constructed from the tracking data of MLC position showed that the measured range of gap size error within -0.3 mm to 0.8 mm. The average MLC positional error tracked was 0.07 mm ± 0.14 mm (mean ± 1 SD). Gap size measured from tracking data was within the range of the results obtained from EPID. Tracking the MLC position using the optical tracking system provide a more accurate test method for routine QA of the MLC performance. Over the period of investigation, it was found that the MLC performance was within the tolerance of ± 1 mm recommended by AAPM TG142.

Reliability of the gamma index analysis as a verification method of volumetric modulated arc therapy plans

BackgroundWe investigate the gamma passing rate (GPR) consistency when applying different types of gamma analyses, linacs, and dosimeters for volumetric modulated arc therapy (VMAT).MethodsA total of 240 VMAT plans for various treatment sites, which were generated with Trilogy (140 plans) and TrueBeam STx (100 plans), were retrospectively selected. For each VMAT plan, planar dose distributions were measured with both MapCHECK2 and ArcCHECK dosimeters. During the planar dose distribution measurements, the actual multileaf collimator (MLC) positions, gantry angles, and delivered monitor units were recorded and compared to the values in the original VMAT plans to calculate mechanical errors. For each VMAT plan, both the global and local gamma analyses were performed with 3%/3 mm, 2%/2 mm, 2%/1 mm, 1%/2 mm, and 1%/1 mm. The Pearson correlation coefficients (r) were calculated 1) between the global and the local GPRs, 2) between GPRs with the MapCHECK2 and the ArcCHECK dosimeters, 3) and between GPRs and the mechanical errors during the VMAT delivery.ResultsFor the MapCHECK2 measurements, strong correlations between the global and local GPRs were observed only with 1%/2 mm and 1%/1 mm (r > 0.8 with p < 0.001), while weak or no correlations were observed for the ArcCHECK measurement. Between the MapCHECK2 and ArcCHECK measurements, the global GPRs showed no correlations (all with p > 0.05), while the local GPRs showed moderate correlations only with 2%/1 mm and 1%/1 mm for TrueBeam STx (r > 0.5 with p < 0.001). Both the global and local GPRs always showed weak or no correlations with the MLC positional errors except for the GPRs of MapCHECK2 with 1%/2 mm and 1%/1 mm for TrueBeam STx and the GPR of ArcCHECK with 1%/2 mm for Trilogy (r < − 0.5 with p < 0.001).ConclusionsThe GPRs varied according to the types of gamma analyses, dosimeters, and linacs. Therefore, each institution should carefully establish their own gamma analysis protocol by determining the type of gamma index analysis and the gamma criterion with their own linac and their own dosimeter.

A Multi-Institutional Experience of MR-Guided Liver Stereotactic Body Radiation Therapy

PurposeDaily magnetic resonance (MR)–guided radiation has the potential to improve stereotactic body radiation therapy (SBRT) for tumors of the liver. Magnetic resonance imaging (MRI) introduces unique variables that are untested clinically: electron return effect, MRI geometric distortion, MRI to radiation therapy isocenter uncertainty, multileaf collimator position error, and uncertainties with voxel size and tracking. All could lead to increased toxicity and/or local recurrences with SBRT. In this multi-institutional study, we hypothesized that direct visualization provided by MR guidance could allow the use of small treatment volumes to spare normal tissues while maintaining clinical outcomes despite the aforementioned uncertainties in MR-guided treatment. Methods and materialsPatients with primary liver tumors or metastatic lesions treated with MR-guided liver SBRT were reviewed at 3 institutions. Toxicity was assessed using National Cancer Institute Common Terminology Criteria for Adverse Events Version 4. Freedom from local progression (FFLP) and overall survival were analyzed with the Kaplan-Meier method and χ2 test. ResultsThe study population consisted of 26 patients: 6 hepatocellular carcinomas, 2 cholangiocarcinomas, and 18 metastatic liver lesions (44% colorectal metastasis). The median follow-up was 21.2 months. The median dose delivered was 50 Gy at 10 Gy/fraction. No grade 4 or greater gastrointestinal toxicities were observed after treatment. The 1-year and 2-year overall survival in this cohort is 69% and 60%, respectively. At the median follow-up, FFLP for this cohort was 80.4%. FFLP for patients with hepatocellular carcinomas, colorectal metastasis, and all other lesions were 100%, 75%, and 83%, respectively. ConclusionsThis study describes the first clinical outcomes of MR-guided liver SBRT. Treatment was well tolerated by patients with excellent local control. This study lays the foundation for future dose escalation and adaptive treatment for liver-based primary malignancies and/or metastatic disease.

OA034] Error detection sensitivity of a commercially available system for 3D plan verifications

Purpose The complexity of the treatment techniques IMRT (Intensity Modulated Radiation Therapy) and VMAT (Volumetric Modulated Arc Therapy) introduced the requirement of an individual plan verification. The latest development is 3D verification which allows evaluation in terms of 3D gamma values but also a judgment based on differences in dose distributions on individual patient CT data and in dose volume histograms. For a verification tool the error detection sensitivity is important. The purpose of this work is to evaluate the accuracy and the sensitivity of the gamma evaluation of the 3D verification software MobiusFx when a systematic error in one parameter is applied. The results are compared to an established 2D-measurement based method. Methods 11 IMRT and 11 VMAT plans were selected as reference plans. For every reference plan a systematic offset of 1 mm, 2 mm and 3 mm in the same direction was applied to the Multi-Leaf-Collimator (MLC) positions. All plans were irradiated and verified simultaneously with MobiusFx and MatriXX Evolution 2D array measurement. For both verification systems the applied dose distributions were evaluated using the gamma method based on the reference plans. In MobiusFx additionally the MLC position errors were evaluated. Results Regarding the gamma evaluation results, MobiusFx and MatriXX Evolution 2D measurements have almost the same sensitivity: The gamma evaluation of both systems detected shifts of 1 mm in VMAT plans and shifts of 2 mm and larger in IMRT plans. The information of the MLC position error provided by MobiusFx allows further to detect errors down to a shift of 1 mm in IMRT plans, giving an advantage over the MatriXX measurement. Conclusions MobiusFx shows a high error detection sensitivity, comparable to MatriXX Evolution. Through the gamma calculation and the MLC position error it is possible to detect errors down to a shift in MLC positions of 1 mm. It is recommended to inspect all the above parameters during plan verification.

Development of a novel methodology for QA of respiratory-gated and VMAT beam delivery using Octavius 4D phantom

The objective of this study was to develop and evaluate a series of quality assurance (QA) techniques based on Octavius 4D phantom for testing of respiratory-gated treatment delivery, integrity of dose rate vs gantry speed in volumetric-modulated arc therapy (VMAT) commissioning, and multileaf collimator (MLC) positioning accuracy of a linear accelerator. An Octavius 4D phantom capable of rotating with the gantry and recording the detector signal with a sampling rate of 10 Hz was isocentrally set up and an inclinometer was also installed to measure the gantry angle simultaneously. A simple arc test was created and delivered with gating function activated to measure the timing accuracy of the gating window. A tailor-made dose rate vs gantry speed plan was also designed to test the accuracy of measured dose rate, gantry speed, and actual control points. All experiments were conducted while machine log files were collected for comparison. The variations of beam flatness, symmetry, and field size were analyzed as a function of gantry angle to evaluate the influence from the modulation of dose rate and gantry speed. MLC position accuracy was evaluated based on specific garden fence plans. The time of gating window was measured to be less than 10-millisecond deviation from the log data. Gantry backlash was observed and quantified to be 1.72° with an extra stabilization time of 1.16 seconds for a gating arc with gantry speed of 6°/s. In the dose rate vs gantry speed test, the mean deviation between measured gantry angle and log data was less than 0.2° after a time delay of 0.25 second was corrected. The measured dose rate agreed with the log data very well with a mean deviation of 0.05%, and even the transit of modulation was tracked successfully. There was a statistically significant difference on the variation of beam parameters between a VMAT plan and a simple arc plan. The induced MLC position errors were detected with an accuracy of 0.05 mm. The leaf position reproducibility was found to be better than 0.02 mm, whereas the routine MLC position accuracy was better than 0.1 mm. A time-resolved method using Octavius 4D phantom has been developed and proven to be convenient for respiratory gating QA, dose rate vs gantry speed test, and MLC QA. Gating time, dose rate, and gantry speed-induced leave position error could be directly measured with high accuracy after comparison with the machine log data. This study also highlights the capability of the phantom in quantifying the variation of flatness, symmetry, and field size during gantry rotation.

Correlation between gamma passing rate and complexity of IMRT plan due to MLC position errors

PurposeThis study evaluates the correlation between the susceptibility of the γ passing rate of IMRT plans to the multi-leaf collimator (MLC) position errors and a quantitative plan complexity metric. MethodsTwenty patients were selected for this study. For each patient, two IMRT plans were generated using sliding window and step-&-shoot techniques, respectively. Modulation complexity score (MCS) was calculated for all IMRT plans, and symmetric MLC leaf bank errors, ranging from 0.3 mm to 1 mm, were introduced. Original and modified plans were delivered using Varian’s Clinac iX. The obtained dose distribution using ArcCHECK was then compared with the TPS calculated dose distribution of the original plans. 3D gamma analysis was performed for each verification with passing criteria of 2%/2 mm. The γ passing rate decreasing gradient were calculated to evaluate relationship between variation of γ passing rate due to MLC errors and complexity. ResultsA linear regression analysis was applied between γ gradient and complexity, and the results showed a linear correlation (R2 = 0.81 and 0.82 for open and closed MLC error types, respectively) indicating the more complex plans are more susceptible to MLC leaf bank errors. Meanwhile, correlation of re-normalized γ passing rate and complexity for all errors scenarios also presented a strong correlation (r > 0.75). ConclusionThe statistics results revealed variation relationship of dosimetry robust of plans with various complexities to MLC errors. Our results also suggested that the observed susceptibility is independent of the delivery techniques.