Are simple IMRT beams more robust against MLC error? Exploring the impact of MLC errors on planar quality assurance and plan quality for different complexity beams

Wang, Jiazhou; Jin, Xiance; Peng, Jiayuan; Xie, Juanying; Chen, Junchao; Hu, Weigang

doi:10.1120/jacmp.v17i3.6022

Cited by 14 publications

(16 citation statements)

References 26 publications

(32 reference statements)

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“…Total leaf travel was also proposed as a complexity metric with a limited correlation to the GPR . The monitor unit was also investigated as a complexity metric, and its proportionality to the GPR was analyzed . These complexity metrics are a single average value created from the beam parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, the correlation of the substitute with the gamma passing rate (GPR) is examined in order to maintain the criteria of current clinical QA practice. Complexity metrics of IMRT were proposed and investigated by several authors in recent years . The modulation complexity score (MCS) developed by McNiven et al .…”

Section: Introductionmentioning

confidence: 99%

“…Complexity metrics of IMRT were proposed and investigated by several authors in recent years. [7][8][9][10][11][12][13][14] The modulation complexity score (MCS) developed by McNiven et al 7 characterizes the average plan complexity using the beam parameters of leaf positions, degree of irregularity in the field shape, segment weight, and area. The researchers investigated the correlation between the MCS and the delivered dose measured by a twodimensional (2D) diode array in terms of beam parameters such as the number of segments and monitor units (MUs).…”

Section: Introductionmentioning

confidence: 99%

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Predictive gamma passing rate by dose uncertainty potential accumulation model

et al. 2018

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Purpose Intensity‐modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification. The purpose of this study is to develop a method to predict the gamma passing rate (GPR) using a dose uncertainty accumulation model. Methods Thirty‐three intensity‐modulated (IM) beams for head‐and‐neck cases with step‐and‐shoot techniques were used in this study. The treatment plan was created using the XiO treatment planning system (TPS). The IM beam was produced by the ONCOR Impression Plus linear accelerator. MapCHECK was used to measure the dose distribution. The distribution of a dose uncertainty potential (DUP) was generated by in‐house software that accumulated field shapes weighted by a segmental monitor unit, followed by Gaussian folding. The width of the Gaussian was determined from the width of the lateral penumbra. The dose difference between the calculated and measured doses was compared with the estimated DUP at each point. The GPR of each beam was predicted for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances by its own DUP histogram and a GPR‐vs‐DUP correlation of other beams using the leave‐one‐out cross‐validation method. The predicted GPR was compared with the measured GPR to evaluate the performance of this prediction method. The criteria for the predicted GPR corresponding to a measured GPR ≥ 90% were estimated to examine the feasibility of estimating the measured GPR by this GPR prediction method. Results The DUP was confirmed to have proportionality to the standard deviation (SD) of the dose difference. The SDs of the difference between the measured and predicted GPRs were 3.1, 1.7, and 1.4% for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances, respectively. The criteria of the predicted GPR corresponding to the measured GPR ≥ 90% were 94.1 and 95.0% with confidence levels of 99 and 99.9%, respectively. Conclusion In this study, we confirmed the good proportionality between the dose difference and the estimated DUP. The results showed a feasibility to predict the dose difference from DUP as estimated by a DUP accumulation model. The predicted GPR developed in this study showed good accuracy for planar dose distributions of head and neck IMRT. The prediction method developed in this study is considered to be feasible as a substitute for the current practice of measurement‐based verification of the dose distribution with gamma analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive gamma passing rate by dose uncertainty potential accumulation model

et al. 2018

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“…Recently, a variety of attempts have been made to estimate the IM complexity and to investigate their relationship between the IM and gamma passing rate (GPR), which may potentially help to omit the patient-specific QA and to improve the efficiency of the clinical QA practice. A complexity metric (CM) that may be related to the GPR has been proposed by several authors, [3][4][5][6][7][8][9][10] resulting in limited predictability of the GPR. Deep learning (DL)-based prediction showed a good performance for the planar dose distribution measured using the film.…”

Section: Introductionmentioning

confidence: 99%

Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Shiba¹,

Saito²,

Furumi³

et al. 2020

Medical Physics

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Purpose We aim to develop a method to predict the gamma passing rate (GPR) of a three‐dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model. Methods Sixty head‐and‐neck intensity‐modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step‐and‐shoot beams of the ONCOR linear accelerator. Verification plans were created and measured by the Delta4 system. The planar DUP (pDUP) manifesting on a field edge was generated from the segmental aperture shape with a Gaussian folding on the beam's‐eye view. The DUP at each voxel (u) was calculated by projecting the pDUP on the Delta4 phantom with its attenuation considered. The learning model (LM), an average GPR as a function of the DUP, was approximated by an exponential function aGPRu=e-qu to compensate for the low statistics of the learning data due to a finite number of the detectors. The coefficient q was optimized to ensure that the difference between the measured and predicted GPRs (dGPR) was minimized. The standard deviation (SD) of the dGPR was evaluated for the optimized LM. Results It was confirmed that the coefficient q was larger for tighter tolerance. This result corresponds to the expectation that the attenuation of the aGPRu will be large for tighter tolerance. The pGPR and mGPR were observed to be proportional for all tolerances investigated. The SD of dGPR was 2.3, 4.1, and 6.7% for tolerances of 3%/3 mm, 3%/2 mm, 2%/2 mm, respectively. Conclusion The DUP‐based predicting method of the GPR was extended to 3D by introducing DUP attenuation and an optimized analytical LM to compensate for the low statistics of the learning data due to a finite number of detector elements. The precision of the predicted GPR is expected to be improved by improving the LM and by involving other metrics.

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“…Dynamic conformal arc (DCA) treatments present two advantages over VMAT: dosimetric robustness and a decrease in the total required MU for treatment. The larger apertures used in DCA planning have been shown to increase plan MU efficiency (decrease in MUs) and decrease susceptibility to multileaf collimator (MLC) errors compared to highly complex plans . The use of conformal apertures increases the average target dose per MU compared to a typical VMAT aperture, which irradiates a subset of target voxels at any one time, thus allowing DCA to use substantially fewer MU.…”

Section: Introductionmentioning

confidence: 99%

Intra‐arc binary collimation algorithm for the optimization of stereotactic radiotherapy treatment of multiple metastases with multiple prescriptions

et al. 2018

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Purpose To design and implement a novel treatment planning algorithm based on a modification of dynamic conformal arc (DCA) therapy for the treatment of multiple cranial metastases with variable prescription doses. Methods A workflow was developed in which separate dose matrices were calculated for each target at each control point (i.e., the multileaf collimator (MLC) was fit conformally to that single target). A cost function was used to quantify the relative contributions of each dose matrix in the plan to the overall plan objectives. Simulated annealing was used to allow for the inclusion or exclusion of individual dose matrices at each control point. The exclusion of individual targets at a given control point is termed intra‐arc binary collimation (iABC) in this work and is accomplished by closing the MLCs over the target for a duration specified by simulated annealing optimization. Dynamic collimator motions were employed to minimize the variation between the idealized dose matrices (i.e., perfectly collimated targets) and actual dose matrices (i.e., MLC apertures that include quantities of nontarget tissue due to the relative orientations of targets in the field). An additional simulated annealing optimization was performed to weight the relative contributions of dose at each control point [referred to as the monitor unit distribution (MUD)] to improve compliance with plan objectives. The algorithm was tested on seven previously treated multiple metastases patients and plans were compared to the clinically treated VMAT plans. Results Treatment plans generated with iABC used an average of 2716 (34%) fewer MU in the total plan than VMAT (P = 0.01). All normal tissue metrics for all plans and all patients were clinically acceptable. There were no statistically significant differences in any normal tissue dose metrics. Normalized prescription target coverage accuracy for all targets was 3% better on average for VMAT plans when compared to iABC (P = 0.07), and 14% better on average for iABC when compared to optimized DCA (P = 0.03). Conclusion A novel method of aperture and dose distribution design has been developed to significantly increase the MU efficiency of single isocenter treatment of multiple metastases with variable prescription doses when compared to VMAT, and which improves target coverage accuracy significantly when compared to optimized DCA. By applying a DCA approach to subsets of targets across control points, a hybrid method of treatment delivery has been developed that combines the efficiency of dynamic conformal treatments and the dosimetric flexibility of VMAT.

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Are simple IMRT beams more robust against MLC error? Exploring the impact of MLC errors on planar quality assurance and plan quality for different complexity beams

Cited by 14 publications

References 26 publications

Predictive gamma passing rate by dose uncertainty potential accumulation model

Predictive gamma passing rate by dose uncertainty potential accumulation model

Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Intra‐arc binary collimation algorithm for the optimization of stereotactic radiotherapy treatment of multiple metastases with multiple prescriptions

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