Optimization of the regularization parameter in the Dual Annealing method used for the reconstruction of energy spectrum of electron beam generated by the AQURE mobile accelerator

IF 2.4 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Zeitschrift fur Medizinische Physik Pub Date : 2024-11-01 DOI:10.1016/j.zemedi.2023.03.003
Adam Ryczkowski , Tomasz Piotrowski , Marcin Staszczak , Marcin Wiktorowicz , Przemysław Adrich
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Abstract

Introduction

The shape of the energy spectrum is an essential component of any electron beam Monte Carlo model. Due to specialized equipment and the long measurement time for the direct methods for determining the energy spectrum, attractive alternatives are backward spectrum reconstructions from the measured data. One such approach is solving the first-degree Fredholm integral equation with appropriate regularization. It makes it possible to calculate the depth distribution as the sum of the distributions from monoenergetic beams. This study aims to determine the optimal value of the regularization parameter for the problem of determining the spectrum of the electron beam produced by a mobile accelerator used during intraoperative radiotherapy.

Material and methods

The Geant4 package was used to generate the distributions of deep doses for monoenergetic beams for two models with different degrees of complexity, i.e. simple (theoretical) and full (for the mobile accelerator). The dose distributions for four different shapes of energy spectrum (for each model) were obtained similarly. They were established as the reference data for further calculations. The Dual Annealing optimization method was used to obtain the reconstructed spectrum. The multiple optimizations that differ by the regularization parameter (ranging from 0 to 1) were performed. For each reconstruction, similarity indicators of the energy spectrum and the dose distribution to the referenced data were calculated to determine the optimal regularization parameters.

Results

Optimal regularization parameters determined by similarity indicators for the spectrum and the dose distribution differ for geometry models considered in the study. The regularization parameter for the simple geometry ranged from 0.03 to 0.05, while for full geometry, they were from 0.05 to 0.06. The results for conventional linear accelerators found in the literature range from 0.5 to 1.1.

Conclusion

The Dual Annealing optimization method can be effectively used to solve the Fredholm equation with Tikhonov regularization to reconstruct an electron beam's energy spectrum. The regularization parameter value depends on the beam-forming system. Its value for the mobile accelerator considered in the study ranges from 0.05 to 0.06, depending on the nominal beam energy value.
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优化用于重建 AQURE 移动加速器产生的电子束能谱的双重退火法中的正则化参数。
简介能谱形状是任何电子束蒙特卡罗模型的重要组成部分。由于直接确定能谱的方法需要专门的设备和较长的测量时间,因此从测量数据中重建后向能谱是很有吸引力的替代方法。其中一种方法是通过适当的正则化求解一阶弗雷德霍姆积分方程。这样就可以将深度分布作为单能量光束分布之和进行计算。本研究旨在确定正则化参数的最佳值,以确定术中放疗时使用的移动加速器产生的电子束频谱:使用Geant4软件包为两种复杂程度不同的模型,即简单模型(理论模型)和完整模型(移动加速器模型)生成单能量束的深部剂量分布。四种不同能谱形状(每种模型)的剂量分布也是以类似方式获得的。它们被确定为进一步计算的参考数据。双退火优化法用于获得重建能谱。根据正则化参数(从 0 到 1)的不同,进行了多次优化。对于每次重建,计算能谱和剂量分布与参考数据的相似性指标,以确定最佳正则化参数:结果:根据能谱和剂量分布的相似性指标确定的最佳正则化参数因研究中考虑的几何模型而异。简单几何模型的正则化参数在 0.03 至 0.05 之间,而全几何模型的正则化参数在 0.05 至 0.06 之间。文献中传统线性加速器的正则化参数从 0.5 到 1.1 不等:双重退火优化法可以有效地利用提霍诺夫正则化来求解弗雷德霍姆方程,从而重建电子束能谱。正则化参数值取决于电子束形成系统。研究中考虑的移动加速器的正则化参数值在 0.05 到 0.06 之间,具体取决于标称电子束能量值。
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来源期刊
CiteScore
3.70
自引率
10.00%
发文量
69
审稿时长
65 days
期刊介绍: Zeitschrift fur Medizinische Physik (Journal of Medical Physics) is an official organ of the German and Austrian Society of Medical Physic and the Swiss Society of Radiobiology and Medical Physics.The Journal is a platform for basic research and practical applications of physical procedures in medical diagnostics and therapy. The articles are reviewed following international standards of peer reviewing. Focuses of the articles are: -Biophysical methods in radiation therapy and nuclear medicine -Dosimetry and radiation protection -Radiological diagnostics and quality assurance -Modern imaging techniques, such as computed tomography, magnetic resonance imaging, positron emission tomography -Ultrasonography diagnostics, application of laser and UV rays -Electronic processing of biosignals -Artificial intelligence and machine learning in medical physics In the Journal, the latest scientific insights find their expression in the form of original articles, reviews, technical communications, and information for the clinical practice.
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