Multileaf Collimator Positions Research Articles

Impact of delivery variations on 3D dose distributions for volumetric modulated arc therapy plans of various complexity.

Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files.

This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery. A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model. Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P < 0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria. In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation.

Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer

Background and purposeDynamic trajectory radiotherapy (DTRT) has been shown to improve healthy tissue sparing compared to volumetric arc therapy (VMAT). This study aimed to assess and compare the robustness of DTRT and VMAT treatment-plans for head and neck (H&N) cancer to patient-setup (PS) and machine-positioning uncertainties. Materials and methodsThe robustness of DTRT and VMAT plans previously created for 46 H&N cases, prescribed 50–70 Gy to 95 % of the planning-target-volume, was assessed. For this purpose, dose distributions were recalculated using Monte Carlo, including uncertainties in PS (translation and rotation) and machine-positioning (gantry-, table-, collimator-rotation and multi-leaf collimator (MLC)). Plan robustness was evaluated by the uncertainties’ impact on normal tissue complication probabilities (NTCP) for xerostomia and dysphagia and on dose-volume endpoints. Differences between DTRT and VMAT plan robustness were compared using Wilcoxon matched-pair signed-rank test (α = 5 %). ResultsAverage NTCP for moderate-to-severe xerostomia and grade ≥ II dysphagia was lower for DTRT than VMAT in the nominal scenario (0.5 %, p = 0.01; 2.1 %, p < 0.01) and for all investigated uncertainties, except MLC positioning, where the difference was not significant. Average differences compared to the nominal scenario were ≤ 3.5 Gy for rotational PS (≤ 3°) and machine-positioning (≤ 2°) uncertainties, <7 Gy for translational PS uncertainties (≤ 5 mm) and < 20 Gy for MLC-positioning uncertainties (≤ 5 mm). ConclusionsDTRT and VMAT plan robustness to the investigated uncertainties depended on uncertainty direction and location of the structure-of-interest to the target. NTCP remained on average lower for DTRT than VMAT even when considering uncertainties.

Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device

A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for passing rate analysis. The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average passing rates of VenusX were 98.49% and 91.11%, respectively. The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Integrating cine EPID, dynamic delivery, and the off-axis Winston-Lutz test to enhance quality control in multiple brain metastasis stereotactic radiotherapy

PurposeStereotactic radiotherapy (SRT) has transformed cancer treatment, especially for brain metastases. Ensuring accurate SRT delivery is crucial, with the Winston-Lutz test being an important quality control tool. Off-axis Winston-Lutz (OAWL) tests are designed for accuracy assessment, but most are limited to fixed angles and hampered by local-field shifts caused by suboptimal Multi-Leaf Collimator (MLC) positioning. This study introduces a new OAWL approach for quality control in multi-brain-metastasis SRT. Utilizing cine Electronic Portal Imaging Device (EPID) images, it can be used with dynamic conformal arc (DCA) therapy. However, dynamic OAWL (DOAWL) is prone to more local-field shifts due to dynamic MLC movements. A two-step DOAWL is proposed: step 1 calculates local-field shifts using dynamic MLC movements in the beam-eye view data from the Treatment Planning System (TPS), while step 2 processes cine EPID images with an OAWL algorithm to isolate true deviations. MethodsValidation involved an anthropomorphic head phantom with metallic ball-bearings, Varian TrueBeam STx accelerator delivering six coplanar/non-coplanar DCA beams, cine EPID, and ImageJ's OAWL analysis algorithm. ResultsInherent local-field shifts ranged from 0.11 to 0.49 mm; corrected mean/max EPID-measured displacement was 0.34/1.03 mm. Few points exceeded 0.75/1.0-mm thresholds. ConclusionsThis two-step DOAWL test merges cine-EPID acquisitions, DCA, OAWL, and advanced analysis and offers effective quality control for multi-brain-metastasis SRT. Its routine implementation may also improve physicist knowledge of the treatment precision of their machines.

Delta4-based Dosimetric Error Detection in Volumetric-modulated Arc Therapy: Clinical Significance and Implications.

Volumetric-modulated arc therapy (VMAT) is an efficient method of administering intensity-modulated radiotherapy beams. The Delta4 device was employed to examine patient data. The utility of the Delta4 device in identifying errors for patient-specific quality assurance of VMAT plans was studied in this research. Intentional errors were purposely created in the collimator rotation, gantry rotation, multileaf collimator (MLC) position displacement, and increase in the number of monitor units (MU). The results show that when the characteristics of the treatment plans were changed, the gamma passing rate (GPR) decreased. The largest percentage of erroneous detection was seen in the increasing number of MU, with a GPR ranging from 41 to 92. Gamma analysis was used to compare the dose distributions of the original and intentional error designs using the 2%/2 mm criteria. The percentage of dose errors (DEs) in the dose-volume histogram (DVH) was also analyzed, and the statistical association was assessed using logistic regression. A modest association (Pearson's R-values: 0.12-0.67) was seen between the DE and GPR in all intentional plans. The findings indicated a moderate association between DVH and GPR. The data reveal that Delta4 is effective in detecting mistakes in treatment regimens for head-and-neck cancer as well as lung cancer. The study results also imply that Delta4 can detect errors in VMAT plans, depending on the details of the defects and the treatment plans employed.

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy.

Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148ms. Median gamma pass rate (2%/3mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12Gy for the investigated volume and dose criteria respectively. Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

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.

Acceptance, commissioning and quality assurance of the MRIdian®: Site experience and three years follow-up

PurposeThis study presents the methodology and results of the acceptance and periodical quality controls on the MRIdian®. Materials and methodsThe impact of the magnetic field on other machines was investigated by controlling nearby linacs dose profiles. The image quality of the 0.345T MR scanner was evaluated, also assessing the integrated linear accelerator influence. The photon beams lateral and depth dose profiles were measured in motorized water tanks, along dose rate and output factors, and compared to Monte Carlo (MC) calculations. The isocenter position, gantry angles and multi-leaf collimator (MLC) position were controlled using film dosimetry. Gating latency and dosimetric accuracy were controlled with a dynamic phantom. ResultsThe magnetic field had no significant impact on other nearby linacs. Image quality was within tolerances and did not vary over time. Dose profiles measured showed good agreement with MC data, with maximum differences of 1.3% in-field. Output factors were within 0.8% of calculated values. Imaging and radiative isocenter matched within 0.9±0.4mm over all monthly controls. Gantry rotation was precise within –0.1±0.2°, with an isocenter variation of 1.4±0.3mm diameter. The average MLC position was within 0.4±0.1mm of theoretical value. Finally, the gating latency was 0.14±0.07sec and the gated dose within 0.3% of base value. ConclusionAll results are within the tolerances fixed by ViewRay® and show low variations over 2 years, comforting the use of small margins and gating for high-dose adaptive treatments.

Suitability of Athena Monolithic Active Pixel Sensor for upstream monitoring of 6 MV, 6 FFF and 10 MV treatments

We are developing a thin, real-time radiotherapy verification sensor based on the Athena, a large-scale MAPS. The goal in radiotherapy verification is to measure the multileaf collimator positions and beam intensity to ensure the accuracy and safety of treatment delivery. Previously, results on this have been published. In this paper, we present results that clearly demonstrate that the Athena does not saturate, even at the highest beam intensities in a 6 FFF 10 × 10 cm2 field and thus is suitable for clinical deployment.

Towards 2D dosimetry using monolithic active pixel sensors and a copper grating

Higher energy and intensity X-ray radiotherapy treatments are coming into wider use, having the benefit of requiring fewer treatment fractions and fewer hospital visits per patient. However, small percentage errors in multileaf collimator positioning and dose become bigger problems with higher doses per fraction. Hence, real-time treatment verification becomes essential. Where devices downstream from the patient suffer from scattering in the patient, upstream devices can disturb the therapeutic beam. Here, a method is proposed for performing dosimetry upstream using monolithic active pixel sensors, which can be made thin enough to disturb the beam by <1%. In order to calculate the dose to the tumour, a verification device needs to make a measurement of the photon field. Some photons will Compton scatter an electron in the silicon and generate a signal. However, this signal is obscured by energy deposits from contamination electrons, originating from Compton scattering in the accelerator head and air. Often extensive build-up material is added to verification devices to reduce the electron contamination and enhance the photon signal. However, this leads to degradation of the beam intensity to the patient. Instead we propose using thin strips of 50 µm thick copper in a grating pattern and measuring the difference in the signal with and without it. The contamination electrons are relatively undisturbed by the presence of the thin copper strips and the photon signal generated via Compton scattering is enhanced under the strips. Hence the difference in the two signals mostly consists of energy deposits originating from the therapeutic photons. From this the dose to the patient can be derived. The 50 μm of copper is thin enough to keep the beam attenuation below 1%, but in itself would give a total signal response which consists of 38% contamination electrons. Using the grating technique, we show that the electron contamination can be reduced to 2.6% of the total signal. This allows the photon signal only to be extracted from the data and thus the dose to patient with a very thin upstream detector can be calculated using a Monte Carlo model to extrapolate the photon flux into the tumour.

A customizable, open-source Winston-Lutz system for multi-target, single isocentre radiotherapy

Objective. To present and share an open-source system (phantom and software) for verifying the targeting accuracy of linac-based, single-isocenter, multi-target radiotherapy. This quality assurance test extends the traditional Winston-Lutz test, which considers a single target located at isocentre. Approach. Plans for a 3D-printed phantom are provided, which can be customized to accommodate various target (BB) positions. Given BB positions and gantry/collimator/couch combinations, the software generates multi-leaf collimator positions to facilitate multi-target Winston-Lutz (MTWL) plan creation. The software determines deviations between detected and expected BB positions on MV images resulting from MTWL plan delivery. BBs are located using a Hough circle detection algorithm, which is modified to favour the detection of circles: (1) having a reasonable size, (2) that are contained within the radiation field, and (3) having reasonable pixel intensities. Validation was performed in two ways: (1) using synthetic data with zero targeting errors and (2) by measuring real linac targeting errors and comparing against results obtained using a commercial system. Main results. Validation using the synthetic data yielded a mean (maximum) absolute discrepancy of 0.11 mm (0.21 mm), which is comparable to the synthetic phantom resolution (0.2 mm). The mean (maximum) absolute discrepancy compared to the commercial system is 0.13 mm (0.43 mm). These values are similar to results obtained with repeated deliveries of the same MTWL plan with the same phantom setup. Both validation tests yield reasonable results and are therefore considered successful. The MTWL test was performed independently by three physicists on two linacs to investigate repeatability, resulting in a mean (maximum) absolute discrepancy of 0.14 mm (0.51 mm) among the various attempts. Significance. Successful completion of this quality assurance test, using our customizable and open-source system, provides confidence that multi-target, single isocentre radiotherapy treatments can be delivered with sufficient geometric accuracy according to the chosen tolerance level.

Mitigating the uncertainty in small field dosimetry by leveraging machine learning strategies

Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy and stereotactic body radiation therapy, and thus the need for accurate dosimetry is ever more important. Here we propose to leverage machine learning (ML) strategies to reduce the uncertainties and increase the accuracy in determining small field output factors (OFs). Linac OFs from a Varian TrueBeam STx were calculated either by the treatment planning system (TPS) or measured with a W1 scintillator detector at various multi-leaf collimator (MLC) positions, jaw positions, and with and without contribution from leaf-end transmission. The fields were defined by the MLCs with the jaws at various positions. Field sizes between 5 and 100 mm were evaluated. Separate ML regression models were generated based on the TPS calculated or the measured datasets. Accurate predictions of small field OFs at different field sizes (FSs) were achieved independent of jaw and MLC position. A mean and maximum % relative error of 0.38 ± 0.39% and 3.62%, respectively, for the best-performing models based on the measured datasets were found. The prediction accuracy was independent of contribution from leaf-end transmission. Several ML models for predicting small field OFs were generated, validated, and tested. Incorporating these models into the dose calculation workflow could greatly increase the accuracy and robustness of dose calculations for any radiotherapy delivery technique that relies heavily on small fields.

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.

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.

PurposeAccurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans.MethodsFor this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R 2, and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance.ResultsThe models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045).ConclusionsWe examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

Examination of conversion method of dose distribution of lung cancer IMRT using fluence reversible calculation function in O-ring type linac and C-type linac

Generally, converting irradiation plans between C-arm linacs (C-linac) when the linac fails is possible without recalculating the dose distribution using a treatment planning system (TPS), because they have similar mechanical structure. However, the O-ring-type linac (O-linac) differs from the C-linac in forming the dose distribution. Therefore, if O-linac breaks down, it is necessary to formulate a treatment plan from scratch. In this study, we investigated a method for converting irradiation from an O-linac to a C-linac. Thirty patients with lung cancer who underwent volumetric-modulated arc therapy with an O-linac were included in this study. The O-linac dose distribution was converted into energy fluence by the function of the TPS. The alternative linac multi-leaf collimator (MLC) was then optimized to achieve energy fluence. The homogeneity index, conformity index, and planning treatment volume (D95%, D2%) of the converted plan were compared with the original plan. For organ at risk (OAR), the dose-volume histograms (DVHs) of the lung, esophagus, heart, and spinal cord were evaluated. Additionally, the shapes of the isodose curves were compared using the Dice similarity coefficient (DSC). There was no significant difference between the target and OARs (p > 0.05). The mean DSCs of 30% to 100% isodose curves of the prescribed dose and the isodose ≥ 105% and ≤ 20%were > 0.8 and < 0.8, respectively. Due to the structural differences of MLC, the dose-volume and generation positions were different in the dose range of ≥ 105% and ≤ 20%; hence, DSCs decreased. However, no statistically significant difference in the DVH was identified for either treatment plan. Based on this result, we propose a simple replanning method for performing MLC fitting after converting the dose to the energy fluence.

Development of a plan complexity mitigation algorithm based on gamma passing rate predictions for volumetric-modulated arc therapy.

Volumetric-modulated arc therapy (VMAT) is a complex rotational therapy technique in which highly conformal dose distribution can be realized by varying the speed of gantry rotation, multileaf collimator (MLC) shape, and dose rate. However, the complexity of the technique creates a discrepancy between the calculated and measured doses. Thus, to mitigate the plan complexity in VMAT, this study aimed to develop an algorithm and evaluate its usefulness by conducting a feasibility study. A total of 50 patients who underwent VMAT between September 2015 and December 2020 were arbitrarily selected for this study. Specifically, patients with less than 85% gamma passing rate (GPR) at 5%/1mm or 3%/2mm criterion were selected randomly. Using the GPR prediction model, problematic MLC positions that contribute to a decrease in GPR were identified. Those problematic MLC positions were optimized using a limited nonlinear algorithm under mechanical limitations. Additionally, the dose prescription for the target was re-normalized. The VMAT modulated complexity score (MCSv ), averaged aperture area (AA), and monitor unit per gray (MU/Gy) were evaluated as plan complexity parameters. Calculated doses in patient geometry were evaluated for the target and its surrounding region. In addition, an ArcCHECK cylindrical diode array was used to measure the dose, and GPRs at 5%/1mm and 3%/2mm criteria were evaluated to analyze the difference between the mitigated and original plans. The difference was calculated using the mean±standard deviation. The differences between the MCSv , AA, and MU/cGy values for the mitigated and original plans were 0.8±1.7 (×10-2 ), 42.7±57.9, and -5.6±8.5, respectively. Regarding the calculated dose, the dose volume parameters were consistent within 1% for the target and the surrounding region. The differences between the mitigated and original plans were 1.8±2.9% and 1.3±1.8% for GPRs at 5%/1mm and 3%/2mm, respectively. This feasibility study resulted in the development of an algorithm with the potential to mitigate plan complexity and improve the GPR for VMAT under minor leaf position modifications.

Performance Evaluation of Different Supervised Machine Learning Algorithms in Predicting Linear Accelerator Multileaf Collimator Positioning’s Accuracy Problem

Performance Evaluation of Different Supervised Machine Learning Algorithms in Predicting Linear Accelerator Multileaf Collimator Positioning’s Accuracy Problem

A Mathematical Method to Adjust MLC Leaf End Position for Accurate Dose Calculation in Carbon Ion Beam Radiation Therapy Treatment Planning System.

Introduction We present a mathematical method to adjust the leaf end position for dose calculation correction in the carbon ion radiation therapy treatment planning system. Methods and Materials A straggling range algorism of 400 MeV/n carbon ion beam in nine different multileaf collimator (MLC) materials was conducted to calculate the dose 50% point to derive the offset corrections in the carbon ion treatment planning system (ciPlan). The visualized light field edge position in the treatment planning system is denoted as Xtang.p, and MLC position (Xmlc.p) is defined as the source to leaf end midpoint projection on axis for monitor unit calculation. The virtual source position of energy at 400 MeV/n and straggling range in MLC at different field sizes were used to calculate the dose 50% position on axis. On-axis MLC offset (correction) could then be obtained from the position corresponding to 50% of the central axis dose minus the Xmlc.p. Results The exact MLC position in the carbon ion treatment planning system can be used as an offset to do the correction. The offset correction of pure tungsten is the smallest among the others due to its shortest straggling range of carbon ion beam in MLC. The positions of 50% dose of all MLC materials are always located in between Xtang.p and Xmlc.p under the largest field of 12 cm by 12 cm. Conclusions MLC offset should be adjusted carefully at different field sizes in the treatment planning systems especially of its small penumbra characteristic in the carbon ion beam. It is necessary to find out the dose 50% position for adjusting MLC leaf edge on-axis location in the treatment planning system to reduce dose calculation error.

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.