Analytical approach for determining beam profiles in water phantom of symmetric and asymmetric fields of wedged, blocked, and open photon beams

Birgani, Mohammad Javad Tahmasebi; Chegeni, Nahid; Arvandi, Shole; Razmjoo, Sasan; Zabihzadeh, Mansour; Khezerloo, Davood

doi:10.1120/jacmp.v14i6.3918

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Today, the use of radiotherapy Treatment Planning Sys-tems (TPS) is inevitable. Beam profile and PDD are the parameters used to verify the dose calculation algorithms of TPS [11]. The study of photon and electron beam characteristic is necessary before calibration machine.…”

Section: Resultsmentioning

confidence: 99%

Dose Distribution of Photon Beam by Siemens Linear Accelerator

Xhafa¹,

Mulaj²,

Hodolli³

et al. 2014

IJMPCERO

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The radiation therapy is applied on around 50% of the cancer patients. As we know, before implementing a radiation treatment planning system in the clinic, the dose-calculation measurement must be validated using rigorous, clinically relevant criteria [1]. Percent Depth Doses (PDD), Dose Profile (DP), Open Collimator Factor (OCF) etc., are measured for all numbers of square fields for Treatment Planning System XiO, version 4.7, for 6 and 15 MV photons energies and for 15˚, 30˚, 45˚, 60˚ wedge, which were employed to obtain the profiles in any depth. The measurements were conducted also for different energies of electron beam and TPS calculation algorithms.

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Section: Resultsmentioning

confidence: 99%

Dose Distribution of Photon Beam by Siemens Linear Accelerator

Xhafa¹,

Mulaj²,

Hodolli³

et al. 2014

IJMPCERO

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“…12,13 Third, the shape of the beam profiles varies with beam geometry (depth and field size) and beam modality [flattening-filter (FF) vs flattening-filter-free (FFF)], which makes it difficult to fit the beam profiles with functions that can facilitate analytical deconvolution. 4,14,15 In a previous proof-of-principle paper, the authors investigated the feasibility of photon beam profile deconvolution using an artificial neural network (ANN). 10 The ANN used a sliding window to extract inputs from the measurement and output of the deconvolved value at the center of the window.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a neural network‐based photon beam profile deconvolution method

Mund

Liu

et al. 2020

J Applied Clin Med Phys

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The authors have previously shown the feasibility of using an artificial neural network (ANN) to eliminate the volume average effect (VAE) of scanning ionization chambers (ICs). The purpose of this work was to evaluate the method when applied to beams of different energies (6 and 10 MV) and modalities [flattened (FF) vs unflattened (FFF)], measured with ICs of various sizes. Methods: The three-layer ANN extracted data from transverse photon beam profiles using a sliding window, and output deconvolved value corresponding to the location at the center of the window. Beam profiles of seven fields ranging from 2 × 2 to 10 × 10 cm 2 at four depths (1.5, 5, 10 and 20 cm) were measured with three ICs (CC04, CC13, and FC65-P) and an EDGE diode detector for 6 MV FF and FFF. Similar data for the 10 MV FF beam was also collected with CC13 and EDGE. The EDGE-measured profiles were used as reference data to train and test the ANNs. Separate ANNs were trained by using the data of each beam energy and modality. Combined ANNs were also trained by combining data of different beam energies and/or modalities. The ANN's performance was quantified and compared by evaluating the penumbra width difference (PWD) between the deconvolved and reference profiles. Results: Excellent agreement between the deconvolved and reference profiles was achieved with both separate and combined ANNs for all studied ICs, beam energies, beam modalities, and geometries. After deconvolution, the average PWD decreased from 1-3 mm to under 0.15 mm with separate ANNs and to under 0.20 mm with combined ANN. Conclusions: The ANN-based deconvolution method can be effectively applied to beams of different energies and modalities measured with ICs of various sizes. Separate ANNs yielded marginally better results than combined ANNs. An IC-specific, combined ANN can provide clinically acceptable results as long as the training data includes data of each beam energy and modality.

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Implementation of the validation testing in MPPG 5.a “Commissioning and QA of treatment planning dose calculations–megavoltage photon and electron beams”

Jacqmin

Bredfeldt

Frigo

et al. 2016

J Applied Clin Med Phys

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The AAPM Medical Physics Practice Guideline (MPPG) 5.a provides concise guidance on the commissioning and QA of beam modeling and dose calculation in radiotherapy treatment planning systems. This work discusses the implementation of the validation testing recommended in MPPG 5.a at two institutions. The two institutions worked collaboratively to create a common set of treatment fields and analysis tools to deliver and analyze the validation tests. This included the development of a novel, open‐source software tool to compare scanning water tank measurements to 3D DICOM‐RT Dose distributions. Dose calculation algorithms in both Pinnacle and Eclipse were tested with MPPG 5.a to validate the modeling of Varian TrueBeam linear accelerators. The validation process resulted in more than 200 water tank scans and more than 50 point measurements per institution, each of which was compared to a dose calculation from the institution's treatment planning system (TPS). Overall, the validation testing recommended in MPPG 5.a took approximately 79 person‐hours for a machine with four photon and five electron energies for a single TPS. Of the 79 person‐hours, 26 person‐hours required time on the machine, and the remainder involved preparation and analysis. The basic photon, electron, and heterogeneity correction tests were evaluated with the tolerances in MPPG 5.a, and the tolerances were met for all tests. The MPPG 5.a evaluation criteria were used to assess the small field and IMRT/VMAT validation tests. Both institutions found the use of MPPG 5.a to be a valuable resource during the commissioning process. The validation testing in MPPG 5.a showed the strengths and limitations of the TPS models. In addition, the data collected during the validation testing is useful for routine QA of the TPS, validation of software upgrades, and commissioning of new algorithms.

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Analytical approach for determining beam profiles in water phantom of symmetric and asymmetric fields of wedged, blocked, and open photon beams

Cited by 4 publications

References 17 publications

Dose Distribution of Photon Beam by Siemens Linear Accelerator

Dose Distribution of Photon Beam by Siemens Linear Accelerator

Evaluation of a neural network‐based photon beam profile deconvolution method

Implementation of the validation testing in MPPG 5.a “Commissioning and QA of treatment planning dose calculations–megavoltage photon and electron beams”

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