Comparative analysis of several detectors for the measurement of radiation transmission and leakage from a multileaf collimator

Lárraga-Gutiérrez, J.M.; Cruz, O. O. Galván de la; García‐Garduño, Olivia Amanda; Ballesteros-Zebadúa, Paola

doi:10.1016/j.ejmp.2013.10.006

Cited by 11 publications

(8 citation statements)

References 12 publications

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“…Sikora (6) reported leaf transmission and interleaf leakage values of 0.012 and 10.0 respectively for an MLCi MLC, but did not report details of how these values were determined. Lárraga‐Gutiérrez et al (21) showed that different detectors can give average MLC transmission differences of up to 12.5% (local difference). Due to the limited detail provided, it could not be confirmed if the difference between Sikora's proposed transmission values and the values determined in this study were a result of MLC manufacturing variations, the consequence of using a different detector or a combination of both.…”

Section: Discussionmentioning

confidence: 99%

Determination of MLC model parameters for Monaco using commercial diode arrays

Kinsella

Shields

McCavana

et al. 2016

J Applied Clin Med Phys

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Multileaf collimators (MLCs) need to be characterized accurately in treatment planning systems to facilitate accurate intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT). The aim of this study was to examine the use of MapCHECK 2 and ArcCHECK diode arrays for optimizing MLC parameters in Monaco X‐ray voxel Monte Carlo (XVMC) dose calculation algorithm. A series of radiation test beams designed to evaluate MLC model parameters were delivered to MapCHECK 2, ArcCHECK, and EBT3 Gafchromic film for comparison. Initial comparison of the calculated and ArcCHECK‐measured dose distributions revealed it was unclear how to change the MLC parameters to gain agreement. This ambiguity arose due to an insufficient sampling of the test field dose distributions and unexpected discrepancies in the open parts of some test fields. Consequently, the XVMC MLC parameters were optimized based on MapCHECK 2 measurements. Gafchromic EBT3 film was used to verify the accuracy of MapCHECK 2 measured dose distributions. It was found that adjustment of the MLC parameters from their default values resulted in improved global gamma analysis pass rates for MapCHECK 2 measurements versus calculated dose. The lowest pass rate of any MLC‐modulated test beam improved from 68.5% to 93.5% with 3% and 2 mm gamma criteria. Given the close agreement of the optimized model to both MapCHECK 2 and film, the optimized model was used as a benchmark to highlight the relatively large discrepancies in some of the test field dose distributions found with ArcCHECK. Comparison between the optimized model‐calculated dose and ArcCHECK‐measured dose resulted in global gamma pass rates which ranged from 70.0%–97.9% for gamma criteria of 3% and 2 mm. The simple square fields yielded high pass rates. The lower gamma pass rates were attributed to the ArcCHECK overestimating the dose in‐field for the rectangular test fields whose long axis was parallel to the long axis of the ArcCHECK. Considering ArcCHECK measurement issues and the lower gamma pass rates for the MLC‐modulated test beams, it was concluded that MapCHECK 2 was a more suitable detector than ArcCHECK for the optimization process.PACS number(s): 87.55.Qr

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Section: Discussionmentioning

confidence: 99%

Determination of MLC model parameters for Monaco using commercial diode arrays

Kinsella

Shields

McCavana

et al. 2016

J Applied Clin Med Phys

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“…reported that global normalization can hide low‐dose errors in critical structures where the tolerable dose is close to its limit 21,22 . Moreover, delivery doses in the low‐dose region are affected by MLC transmission 23,24 . Although MLC transmission is affected by detector‐specific properties 24 and depth and field‐size dependence in its dosimetry, 25 it needs to be registered as a single value in TPS.…”

Section: Methodsmentioning

confidence: 99%

“…21,22 Moreover, delivery doses in the low-dose region are affected by MLC transmission. 23,24 Although MLC transmission is affected by detector-specific properties 24 and depth and field-size dependence in its dosimetry, 25 it needs to be registered as a single value in TPS. These effects may cause uncertainty between the calculated and measured doses in the low-dose region, resulting in dose discrepancies.…”

Section: Datasetmentioning

confidence: 99%

Evaluation of prediction and classification performances in different machine learning models for patient‐specific quality assurance of head‐and‐neck VMAT plans

et al. 2021

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The purpose of this study is to evaluate the prediction and classification performances of the gamma passing rate (GPR) for different machine learning models and to select the best model for achieving machine learningbased patient-specific quality assurance (PSQA). Methods: The measurement verification of 356 head-and-neck volumetric modulated arc therapy plans was performed using a diode array phantom (Delta4 Phantom), and GPR values at 2%/2 mm with global normalization and 3%/2 mm with local normalization were calculated. Machine learning models, including ridge regression (RIDGE), random forest (RF), support vector regression (SVR), and stacked generalization (STACKING), were used to predict the GPR. Each machine learning model was trained using 260 plans, and the prediction accuracy was evaluated using the remaining 96 plans. The prediction error between the measured and predicted GPR was evaluated. For the classification evaluation, the lower control limit for the measured GPR and lower control limit for predicted GPR (LCL p ) was defined to identify whether the GPR values represent a "pass" or a "fail." LCL p values with 99% and 99.9% confidence levels were calculated as the upper prediction limits for the GPR estimated from the linear regression between the measured and predicted GPR. Results: There was an overestimation trend of the low measured GPR. The maximum prediction errors for RIDGE, RF, SVR, and STACKING were 3.2%, 2.9%, 2.3%, and 2.2% at the global 2%/2 mm and 6.3%, 6.6%, 6.1%, and 5.5% at the local 3%/2 mm, respectively. In the global 2%/2 mm, the sensitivity was 100% for all the machine learning models except RIDGE when using 99% LCL p . The specificity was 76.1% for RIDGE, RF, and SVR and 66.3% for STACKING; however, the specificity decreased dramatically when 99.9% LCL p was used. In the local 3%/2 mm, however, only STACKING showed 100% sensitivity when using 99% LCL p . The decrease in the specificity using 99.9% LCL p was smaller than that in the global 2%/2 mm, and the specificity for RIDGE, RF, SVR, and STACKING was 61.3%, 61.3%, 72.0%, and 66.8%, respectively. Conclusions: STACKING had better prediction accuracy for low GPR values than other machine learning models. Applying LCL p to a regression model enabled the consistent evaluation of quantitative and qualitative GPR predictions. Adjusting the confidence level of the LCL p helped improve the balance between the sensitivity and specificity. We suggest that STACKING can assist the safe and efficient operation of PSQA.

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“…Here, CI ranges from 0 to 1, and higher CI values indicate better conformity. The HI equation (Chen et al, 2020;Jang et al, 2008;Larraga-Gutierrez et al, 2014;Zhu et al, 2020) is expressed by…”

Section: Plan Evaluationmentioning

confidence: 99%

Cone beam computed tomography and image registration based on target area for stereotactic body radiation therapy of lung cancer

Gui

Miao

2021

Journal of Radiation Research and Applied Sciences

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This paper aims to compare the dosimetric parameters of the original plan and the simulated plan to explore the clinical value of cone beam CT and planned CT image registration based on the target area. The results showed that Dmax, Dmin, dmean and V60 of PTV in the original plan were (108.3 ± 1.1)%, (97.2 ± 0.3)%, (102.6 ± 0.6)% and (95.2 ± 0.6)% respectively. The Dmax of spinal cord was (9.6 ± 1.8) gy, the V20 of lung was (5.2 ± 1.9)%, and the dmean of lung was (− 3.7 ± 1.5) gy; however, when the target area was translated in the three directions of the patient's back, right side and foot, the dosimetric parameters of the target area (Dmax, Dmin and V60), dmean and V20 of lung and Dmax of spinal cord showed slight differences, which all met the limit requirements of target area and dangerous organs (OARs) (RTOG) 0915 report. Therefore, target based image registration is feasible. If the registration translation error is less than or equal to 1 cm, there is no need to re move the treatment bed according to the registration error.

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Comparative analysis of several detectors for the measurement of radiation transmission and leakage from a multileaf collimator

Cited by 11 publications

References 12 publications

Determination of MLC model parameters for Monaco using commercial diode arrays

Determination of MLC model parameters for Monaco using commercial diode arrays

Evaluation of prediction and classification performances in different machine learning models for patient‐specific quality assurance of head‐and‐neck VMAT plans

Cone beam computed tomography and image registration based on target area for stereotactic body radiation therapy of lung cancer

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