Feasibility study of using flattening-filter-free photon beams to deliver conventional flat beams.

Various dosimetric benefits such as increased dose rate, and reduced leakage and out of field dose have led to the growth of flattening-filter-free (FFF) technology in the clinic. In this study, we concentrate on investigating the feasibility of using FFF beams to deliver conventional flat beams, since completely getting rid of the flattening-filter module from the gantry head can not only simplify the gantry design but also decrease the workload on machine maintenance and quality assurance. Two intensity modulated radiotherapy techniques, step-and-shoot (S&S) and sliding window (SW), were used to generate flat beam profiles for 6 regular-shaped beams and 3 clinical beams while operating in FFF mode. The inverse plans were generated based on uniform dose optimization. Degree of flatness, MU efficiency, and beam delivery time for both methods were assessed. S&S technique is able to achieve a degree of flatness less than 2.5% for most field configurations. While SW technique was able to generate relatively flat beams for field sizes less than 18 × 18 cm2. For all field configurations, S&S beams resulted in a longer delivery time compared to reference flat beams and SW beams. For field sizes less than 18 × 18 cm2, SW modulated FFF beams resulted in a faster delivery time compared to reference flat beams. The ability to deliver conventional flat beams is not absent when operating in FFF mode. Utilizing beam modulation, FFF mode can achieve reasonable flat profiles and comparable efficiency to conventional flat beams. The ability to deliver most clinical treatments from the same treatment unit will allow for less quality assurance as well as maintenance, and completely eliminate the need for the flattening filter on modern linacs.

Feasibility of photon beam profile deconvolution using a neural network.

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.

A universal parameterized gradient-based method for photon beam field size determination.

PURPOSE: To propose a universal, parameterized gradient-based method (PGM) for radiation field size determination. METHODS: The PGM locates the beam profile's edge by parameterizing its penumbra region with a modified sigmoid function where the inflection point can be determined in a closed form. The parametrization was validated with filter-flattened (FF), flattening-filter-free (FFF) and wedged profiles measured on two Elekta linac models (Synergy and Versa HD). Gamma analysis with the delta dose function set to zero was used to quantitatively assess the parameterization accuracy. Field sizes of FF beams were calculated with the PGM and the full width at half maximum (FWHM) methods for comparison. To assess the consistency of the PGM and the FWHM method with geometric scaling across different depths, the calculated field size at a reference depth was scaled to other depths and compared with the field sizes calculated from the measured profiles. The method was also validated against a maximum-slope method (MSM) with wedge and FFF profiles. We also evaluated the robustness of the three methods with respect to measurement noise, varying scanning step sizes, detector characteristics, and beam energy/modality. RESULTS: Small distance-to-agreement (0.02 ± 0.02 mm) between the measured and parameterized penumbra region was observed for all profiles. The differences between the field sizes calculated with the FWHM method and the PGM were consistent (0.9 ± 0.3 mm), with the FWHM method yielding larger values. With geometrical scaling, the PGM and the FWHM method produced maximum differences of 0.26 and 1.16 mm, respectively. For wedge and FFF beams, the mean differences relative to FF fields were 0.15 ± 0.09 mm and 0.57 ± 0.91 mm for the PGM and the MSM, respectively. The PGM was also found to produce more consistent results than the FWHM method and the MSM when measurement noise, scanning step size, detector characteristics, and beam energy/modality changed. CONCLUSION: The proposed PGM is universally applicable to all beam modalities (FF, wedge and FFF) for accurate field size determination. Compared to the FWHM and the MSM, it is more robust to variations in measurement condition and detection system.

Technical Note: Impact of the geometry dependence of the ion chamber detector response function on a convolution-based method to address the volume averaging effect.

PURPOSE: To investigate the geometry dependence of the detector response function (DRF) of three commonly used scanning ionization chambers and its impact on a convolution-based method to address the volume averaging effect (VAE). METHODS: A convolution-based approach has been proposed recently to address the ionization chamber VAE. It simulates the VAE in the treatment planning system (TPS) by iteratively convolving the calculated beam profiles with the DRF while optimizing the beam model. Since the convolved and the measured profiles are subject to the same VAE, the calculated profiles match the implicit "real" ones when the optimization converges. Three DRFs (Gaussian, Lorentzian, and parabolic function) were used for three ionization chambers (CC04, CC13, and SNC125c) in this study. Geometry dependent/independent DRFs were obtained by minimizing the difference between the ionization chamber-measured profiles and the diode-measured profiles convolved with the DRFs. These DRFs were used to obtain eighteen beam models for a commercial TPS. Accuracy of the beam models were evaluated by assessing the 20%-80% penumbra width difference (PWD) between the computed and diode-measured beam profiles. RESULTS: The convolution-based approach was found to be effective for all three ionization chambers with significant improvement for all beam models. Up to 17% geometry dependence of the three DRFs was observed for the studied ionization chambers. With geometry dependent DRFs, the PWD was within 0.80 mm for the parabolic function and CC04 combination and within 0.50 mm for other combinations; with geometry independent DRFs, the PWD was within 1.00 mm for all cases. When using the Gaussian function as the DRF, accounting for geometry dependence led to marginal improvement (PWD < 0.20 mm) for CC04; the improvement ranged from 0.38 to 0.65 mm for CC13; for SNC125c, the improvement was slightly above 0.50 mm. CONCLUSIONS: Although all three DRFs were found adequate to represent the response of the studied ionization chambers, the Gaussian function was favored due to its superior overall performance. The geometry dependence of the DRFs can be significant for clinical applications involving small fields such as stereotactic radiotherapy.

Parameterization of photon beam dosimetry for a linear accelerator.

PURPOSE: In radiation therapy, accurate data acquisition of photon beam dosimetric quantities is important for (1) beam modeling data input into a treatment planning system (TPS), (2) comparing measured and TPS modeled data, (3) the quality assurance process of a linear accelerator's (Linac) beam characteristics, (4) the establishment of a standard data set for comparison with other data, etcetera. Parameterization of the photon beam dosimetry creates a data set that is portable and easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon beam dosimetric quantities, including percentage depth doses (PDDs), profiles, and total scatter output factors (S(cp)). METHODS: S(cp), PDDs, and profiles for different field sizes, depths, and energies were measured for a Linac using a cylindrical 3D water scanning system. All data were smoothed for the analysis and profile data were also centered, symmetrized, and geometrically scaled. The S(cp) data were analyzed using an exponential function. The inverse square factor was removed from the PDD data before modeling and the data were subsequently analyzed using exponential functions. For profile modeling, one halfside of the profile was divided into three regions described by exponential, sigmoid, and Gaussian equations. All of the analytical functions are field size, energy, depth, and, in the case of profiles, scan direction specific. The model's parameters were determined using the minimal amount of measured data necessary. The model's accuracy was evaluated via the calculation of absolute differences between the measured (processed) and calculated data in low gradient regions and distance-to-agreement analysis in high gradient regions. Finally, the results of dosimetric quantities obtained by the fitted models for a different machine were also assessed. RESULTS: All of the differences in the PDDs' buildup and the profiles' penumbra regions were less than 2 and 0.5 mm, respectively. The differences in the low gradient regions were 0.20% ± 0.20% (<1% for all) and 0.50% ± 0.35% (<1% for all) for PDDs and profiles, respectively. For S(cp) data, all of the absolute differences were less than 0.5%. CONCLUSIONS: This novel analytical model with minimum measurement requirements was proved to accurately calculate PDDs, profiles, and S(cp) for different field sizes, depths, and energies.

A novel convolution-based approach to address ionization chamber volume averaging effect in model-based treatment planning systems.

The ionization chamber volume averaging effect is a well-known issue without an elegant solution. The purpose of this study is to propose a novel convolution-based approach to address the volume averaging effect in model-based treatment planning systems (TPSs). Ionization chamber-measured beam profiles can be regarded as the convolution between the detector response function and the implicit real profiles. Existing approaches address the issue by trying to remove the volume averaging effect from the measurement. In contrast, our proposed method imports the measured profiles directly into the TPS and addresses the problem by reoptimizing pertinent parameters of the TPS beam model. In the iterative beam modeling process, the TPS-calculated beam profiles are convolved with the same detector response function. Beam model parameters responsible for the penumbra are optimized to drive the convolved profiles to match the measured profiles. Since the convolved and the measured profiles are subject to identical volume averaging effect, the calculated profiles match the real profiles when the optimization converges. The method was applied to reoptimize a CC13 beam model commissioned with profiles measured with a standard ionization chamber (Scanditronix Wellhofer, Bartlett, TN). The reoptimized beam model was validated by comparing the TPS-calculated profiles with diode-measured profiles. Its performance in intensity-modulated radiation therapy (IMRT) quality assurance (QA) for ten head-and-neck patients was compared with the CC13 beam model and a clinical beam model (manually optimized, clinically proven) using standard Gamma comparisons. The beam profiles calculated with the reoptimized beam model showed excellent agreement with diode measurement at all measured geometries. Performance of the reoptimized beam model was comparable with that of the clinical beam model in IMRT QA. The average passing rates using the reoptimized beam model increased substantially from 92.1% to 99.3% with 3%/3 mm and from 79.2% to 95.2% with 2%/2 mm when compared with the CC13 beam model. These results show the effectiveness of the proposed method. Less inter-user variability can be expected of the final beam model. It is also found that the method can be easily integrated into model-based TPS.