Energy response corrections for profile measurements using a combination of different detector types

Wegener, Sonja; Sauer, Otto A.

doi:10.1002/mp.12706

Cited by 8 publications

(18 citation statements)

References 37 publications

(113 reference statements)

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“…The choice of 15 mm for the sliding window width indicates that the detector response function has a nonzero valued tail reaching beyond the physical dimension of the ionization chamber. This phenomenon, which can be explained by the secondary electron transport in the surrounding medium and the perturbation of the secondary electron field attributable to the detector presence, has been noted by several research groups . For example, Wegener and Sauer achieved satisfying results in their study where a Gaussian function of 12 mm spread was used to model the response of a 0.125 mm 3 ionization chamber with a 5.5 mm inner diameter (Semiflex 31010, PTW, Germany) .…”

Section: Discussionmentioning

confidence: 91%

“…Another limitation of this study is the use of EDGE as the reference detector. EDGE also introduces VAE, albeit small . Gafchromic film such as EBT3 can provide better approximation of the real beam profile, especially in the penumbra.…”

Section: Discussionmentioning

confidence: 99%

“…This phenomenon, which can be explained by the secondary electron transport in the surrounding medium and the perturbation of the secondary electron field attributable to the detector presence, has been noted by several research groups. 13,21,25 For example, Wegener and Sauer achieved satisfying results in their study where a Gaussian function of 12 mm spread was used to model the response of a 0.125 mm 3 ionization chamber with a 5.5 mm inner diameter (Semiflex 31010, PTW, Germany). 25 Ketelhut and Kapsch used both measurement and Monte Carlo simulation to study the response of Semiflex 31010.…”

Section: Depth (Cm)mentioning

confidence: 98%

“…13,21,25 For example, Wegener and Sauer achieved satisfying results in their study where a Gaussian function of 12 mm spread was used to model the response of a 0.125 mm 3 ionization chamber with a 5.5 mm inner diameter (Semiflex 31010, PTW, Germany). 25 Ketelhut and Kapsch used both measurement and Monte Carlo simulation to study the response of Semiflex 31010. 21 They also found that its response function had a span of approximately 16 mm.…”

Section: Depth (Cm)mentioning

confidence: 98%

“…EDGE also introduces VAE, albeit small. 25,27 Gafchromic film such as EBT3 can provide better approximation of the real beam profile, especially in the penumbra. However, film has energy dependence and absorbed dose dependence in addition to the uncertainties in the process of irradiation and scanning.…”

Section: Depth (Cm)mentioning

confidence: 99%

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Feasibility of photon beam profile deconvolution using a neural network

Liu

Park

et al. 2018

Medical Physics

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Purpose Ionization chambers are the detectors of choice for photon beam profile scanning. However, they introduce significant volume averaging effect (VAE) that can artificially broaden the penumbra width by 2–3 mm. The purpose of this study was to examine the feasibility of photon beam profile deconvolution (the elimination of VAE from ionization chamber‐measured beam profiles) using a three‐layer feedforward neural network. Methods Transverse beam profiles of photon fields between 2 × 2 and 10 × 10 cm2 were collected with both a CC13 ionization chamber and an EDGE diode detector on an Elekta Versa HD accelerator. These profiles were divided into three datasets (training, validation and test) to train and test a three‐layer feedforward neural network. A sliding window was used to extract input data from the CC13‐measured profiles. The neural network produced the deconvolved value at the center of the sliding window. The full deconvolved profile was obtained after the sliding window was moved over the measured profile from end to end. The EDGE‐measured beam profiles were used as reference for the training, validation, and test. The number of input neurons, which equals the sliding window width, and the number of hidden neurons were optimized with a parametric sweeping method. A total of 135 neural networks were fully trained with the Levenberg–Marquardt backpropagation algorithm. The one with the best overall performance on the training and validation dataset was selected to test its generalization ability on the test dataset. The agreement between the neural network‐deconvolved profiles and the EDGE‐measured profiles was evaluated with two metrics: mean squared error (MSE) and penumbra width difference (PWD). Results Based on the two‐dimensional MSE plots, the optimal combination of sliding window width of 15 and 5 hidden neurons was selected for the final neural network. Excellent agreement was achieved between the neural network‐deconvolved profiles and the reference profiles in all three datasets. After deconvolution, the mean PWD reduced from 2.43 ± 0.26, 2.44 ± 0.36, and 2.46 ± 0.29 mm to 0.15 ± 0.15, 0.04 ± 0.03, and 0.14 ± 0.09 mm for the training, validation, and test dataset, respectively. Conclusions We demonstrated the feasibility of photon beam profile deconvolution with a feedforward neural network in this work. The beam profiles deconvolved with a three‐layer neural network had excellent agreement with diode‐measured profiles.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Depth (Cm)mentioning

confidence: 98%

Section: Depth (Cm)mentioning

confidence: 98%

Section: Depth (Cm)mentioning

confidence: 99%

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Feasibility of photon beam profile deconvolution using a neural network

Liu

Park

et al. 2018

Medical Physics

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Validation of virtual water phantom software for pre‐treatment verification of single‐isocenter multiple‐target stereotactic radiosurgery

Calvo-Ortega

Greer

Hermida-López

et al. 2021

J Applied Clin Med Phys

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The aim of this study was to benchmark the accuracy of the VIrtual Phantom Epid dose Reconstruction (VIPER) software for pre-treatment dosimetric verification of multiple-target stereotactic radiosurgery (SRS). VIPER is an EPID-based method to reconstruct a 3D dose distribution in a virtual phantom from in-air portal images. Validation of the VIPER dose calculation was assessed using several MLC-defined fields for a 6 MV photon beam. Central axis percent depth doses (PDDs) and output factors were measured with an ionization chamber in a water tank, while dose planes at a depth of 10 cm in a solid flat phantom were acquired with radiochromic films. The accuracy of VIPER for multiple-target SRS plan verification was benchmarked against Monte Carlo simulations. Eighteen multiple-target SRS plans designed with the Eclipse treatment planning system were mapped to a cylindrical water phantom. For each plan, the 3D dose distribution reconstructed by VIPER within the phantom was compared with the Monte Carlo simulation, using a 3D gamma analysis. Dose differences (VIPER vs. measurements) generally within 2% were found for the MLC-defined fields, while film dosimetry revealed gamma passing rates (GPRs) ≥95% for a 3%/1 mm criteria. For the 18 multiple-target SRS plans, average 3D GPRs greater than 93% and 98% for the 3%/2 mm and 5%/2 mm criteria, respectively. Our results validate the use of VIPER as a dosimetric verification tool for pre-treatment QA of single-isocenter multiple-target SRS plans. The method requires no setup time on the linac and results in an accurate 3D characterization of the delivered dose.

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Multi‐institution validation of a new high spatial resolution diode array for SRS and SBRT plan pretreatment quality assurance

et al. 2020

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The SRS MapCHECK â , a recently developed patient-specific quality assurance (PSQA) tool for end-to-end testing of stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT), was evaluated in a multi-institution study and compared with radiochromic film. Methods: The SRS MapCHECK was used to collect data on 84 SBRT or SRS PSQA plans/fields at nine institutions on treatment delivery devices (TDD) manufactured by Varian and Elekta. PSQA plans from five different treatment planning software (TPS) were selected and executed on TDDs operating at beam energies of 6 and 10 MV with and without a flattening filter. The patient plans were all VMAT except for ten conformal arc therapy fields. The plans were selected to encompass a range of size and tumor sites including brain, lung, spine, abdomen, ear, pancreas, and liver. Corresponding radiochromic film data was acquired in 50 plans/fields. Results were evaluated using gamma analysis with absolute dose criterion of 3% global dose-difference (DD) and 1 mm distanceto-agreement (DTA). Results: The mean 3% DD/1 mm DTA Gamma pass rate of SRS MapCHECK in comparison to film was 95.9%, whereas comparison of SRS MapCHECK to the treatment planning software was 94.7%. 80% of SRS MapCHECK comparisons against film exceed 95% pass rate, and about 30% of SRS MapCHECK comparisons against film exceed 99% pass rate. To maintain good agreement between SRS MapCHECK and film or TPS, authors recommend avoiding plans with a modified modulation complexity score (MMCS) <0.1 arbitrary units (a.u.). In the examples presented, this coincides with avoiding plans with a mu/dose limit of >3 µ/cGy. Conclusions: Stereotactic radiosurgery MapCHECK has been validated for PSQA for a variety of clinical SRS/SBRT plans in a wide range of treatment delivery conditions. The SRS MapCHECK comparison with film demonstrates near-equivalence for analysis of patient-specific QA deliveries comprised of small field measurements.

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Energy response corrections for profile measurements using a combination of different detector types

Cited by 8 publications

References 37 publications

Feasibility of photon beam profile deconvolution using a neural network

Feasibility of photon beam profile deconvolution using a neural network

Validation of virtual water phantom software for pre‐treatment verification of single‐isocenter multiple‐target stereotactic radiosurgery

Multi‐institution validation of a new high spatial resolution diode array for SRS and SBRT plan pretreatment quality assurance

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