Experimental determination of the lateral dose response functions of detectors to be applied in the measurement of narrow photon-beam dose profiles

Poppinga, Daniela; Meyners, Jutta; Delfs, Björn; Muru, A; Harder, Dietrich; Poppe, Björn; Looe, Hui Khee

doi:10.1088/0031-9155/60/24/9421

Cited by 24 publications

(47 citation statements)

References 51 publications

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“…In this work, the improvement of profile measurements was experimentally measured against EBT3 Gafchromic film, which was assumed to yield the true profile in water. Film was previously used as the reference in other studies under similar conditions at field edges . Yet, film dosimetry does have its limitations far out of the field.…”

Section: Discussionmentioning

confidence: 99%

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

Wegener

Sauer

2017

Medical Physics

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Section: Discussionmentioning

confidence: 99%

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

Wegener

Sauer

2017

Medical Physics

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“…The experimental setup of slit beam measurements was done analogously as described in Poppinga et al . at an Artiste and Primus linear accelerator (Siemens Healthcare, Erlangen, Germany).…”

Section: Methodsmentioning

confidence: 99%

“…High‐resolution compact detectors are recommended for small field output factor measurements . The most common of these being silicon diodes, or more recently synthetic diamond detector, and commercial scintillation detector . Air‐filled ionization chambers are considered less suitable, which is attributed to (a) the geometrical volume‐averaging effect owing to the physical dimensions of the effective sensitive air cavity and (b) the perturbation of the fluence of secondary electrons due to the low density air cavity and other nonwater equivalent components such as the chamber wall and the inner electrode .…”

Section: Introductionmentioning

confidence: 99%

“…A mathematical approach useful to quantify the role of the volume effect in small field dosimetry is the convolution model , represented by the convolution integral

M false(x, y false) = \int_{- \infty}^{+ \infty} \int_{- \infty}^{+ \infty} K false(x - ξ, y - η false) D false(ξ, η false) d ξ d η

which transforms a given lateral dose profile, D ( x , y ), into the distorted lateral signal profile, M ( x , y ), by convolution with the detector's lateral dose–response function, K ( x , y ) . The detector‐specific convolution kernel K ( x , y ) (free from polarity effect) has been shown useful to characterize the physical effects underlying both the small field output correction factors and the distortion of the signal profiles due to the volume effect associated with the detector's construction.…”

Section: Introductionmentioning

confidence: 99%

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The polarity effect of compact ionization chambers used for small field dosimetry

et al. 2018

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Purpose The recent developments of compact air‐filled ionization chambers for use in small photon beams have raised the needs to address the associated polarity effect. The polarity effect of five compact ionization chambers has been quantified at small field sizes. The origins of the polarity effect are studied experimentally and through Monte‐Carlo simulations. For this purpose, the one‐dimensional lateral dose–response functions were determined using positive and negative chamber's polarity. Monte‐Carlo simulations were performed to study the underlying mechanism of the polarity effect by quantifying the charge imbalance in the collecting electrode and cable. Methods Five novel compact ionization chamber designs have been studied (PTW‐Freiburg: Semiflex 3D 31021, PinPoint 3D 31022 and PinPoint 31023; IBA Dosimetry: Razor chamber CC01‐G and Razor Nano‐chamber CC003). Output ratios were measured down to a nominal field side length of 3 mm using both polarities to evaluate the polarity effect at different field sizes. The small field output correction factors were derived using a scintillator detector as reference. To identify the origins of the polarity effect, slit beam measurements were performed to obtain their lateral dose–response functions. All measurements were performed using three chamber orientations: axial, radial crossplane, and radial inplane. The chambers were modeled according to the manufacturers' blueprints using the Monte‐Carlo package EGSnrc. The charge imbalance due to electrons entering and leaving the inner electrode and cable was studied using an adapted user‐code. Results The output ratios obtained using all five chambers show field size‐dependent polarity effects at small field sizes in the axial orientation, whereas no significant field size dependence of the polarity effect has been observed in the radial orientations. The chambers' lateral dose–response functions reveal that the radiation‐induced charge imbalance in the inner electrode and cable is the main cause of the observed polarity effect at small field sizes. The effect is weakest for the largest PTW 31021 chamber but intensifies for smaller chambers with decreasing sensitive air volume. Consistent results have been obtained between Monte‐Carlo simulations and measurement data. Conclusions Awareness needs to be raised so that the polarity effect of novel compact ionization chambers is appropriately accounted for in small field dosimetry. The results in this work are useful to identify the magnitude of the polarity effect correction and to assist in the decisions on choosing the appropriate chambers and setups during measurements. The origins of the observed polarity effect have been elucidated using the chambers' lateral dose–response functions. The adapted Monte‐Carlo user‐code has been used to compute the radiation‐induced charge imbalance in the chamber's components. It opens the possibility to study the chamber's design with the aim to minimize its polarity effect. Small field output correction factors computed according to...

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“…The motivation of this work comes from the convolution model relating the real beam profile to the measured beam profile . The photon beam profile measurement process can be described with a convolution equation,

P_{m} false(x false) = P_{r} false(x false) \otimes K false(x false),

where ⊗ denotes the convolution operation, x is the location along the scanning direction and P m ( x ), P r ( x ), and K ( x ) are the measured beam profile, the real beam profile, and the detector response function, respectively.…”

Section: Methodsmentioning

confidence: 99%

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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Experimental determination of the lateral dose response functions of detectors to be applied in the measurement of narrow photon-beam dose profiles

Cited by 24 publications

References 51 publications

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

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

The polarity effect of compact ionization chambers used for small field dosimetry

Feasibility of photon beam profile deconvolution using a neural network

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