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

Abstract

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.

Feasibility of photon beam profile deconvolution using a neural network

“…Relatively speaking, the clinical impact of VAE is less significant in larger fields (>10 × 10 cm 2 ). In the beam model of commercial treatment planning systems, the parameters responsible for the penumbra area are typically determined based on beam profiles of fields with sizes between 2 × 2 and 10 × 10 cm 2 . Additionally, the EDGE diode detector, which was used to collect reference data, over‐responds to the increasing low energy scattered photons in large fields .…”

“…In this work, a 0.13 cm 3 ionization chamber (CC13, Scanditronix Wellhofer, Bartlett, TN) with a 6 mm diameter was used to collect beam profiles that were subsequently resampled at 1 mm resolution. The response of CC13 is typically characterized with a truncated Gaussian function with full width at half maximum approximately equal to the diameter of the chamber's active volume . The spread of K ( x ) exceeds the detector's physical dimension due to the secondary electron transport and the perturbation of detector presence, which can be approximated by the long tail of the Gaussian function.…”

“…While the CC13 suffers from VAE due to its large size, the EDGE diode has negligible VAE owing to its much smaller active measuring area (0.8 × 0.8 mm 2 ). Previous studies have used diode‐measured beam profiles as reference for beam profile deconvolution . Therefore, while the input data for the neural network was extracted from CC13‐measured beam profiles with a moving window, the desired output for each input was taken from the EDGE‐measured beam profile at the location corresponding to the center of the moving window.…”

“…The VAE, characterized by the detector response function, is eliminated in the frequency domain based on the convolution theory . The pros and cons of the approaches in both categories have been discussed extensively in the literature …”

“…3,[6][7][8][9] In the literature, there are two categories of approaches to achieve photon beam profile deconvolution. 10 The first category includes extrapolation-based approaches that use multiple detectors of various sizes to measure the same profile, from which the profile corresponding to a zero-sized detector is extrapolated. 4,[11][12][13] Methods in the second category uses the theory that the measured profile can be regarded as the convolution between the true beam profile and the detector response function.…”