Dosimetric Evaluation of the Treatment Plan on Indigenous Heterogeneous Phantoms using Analytical Anisotropic Algorithm and Acuros-XB Algorithm for Different Photon Energies
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Background
Modern radiotherapy techniques are using advanced algorithms; however, phantoms used for quality assurance have homogeneous density; accordingly, the development of heterogeneous phantom mimicking human body sites is imperative to examine variation between planned and delivered doses. Objective
This study aimed to analyze the accuracy of planned dose by different algorithms using indigenously developed heterogeneous thoracic phantom (HT). Material and Methods
In this experimental study, computed tomography (CT) of HT was done, and the density of different parts was measured. The plan was generated on CT images of HCP with 6 and 15 Megavoltage (MV) photon beams using different treatment techniques, including three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Plans were delivered by the linear accelerator, and the dose was measured using the ion chamber (IC) placed in HT; planned and measured doses were compared. Results
Density patterns for different parts of the fabricated phantom, including rib, spine, scapula, lung, chest wall, and heart were 1.849, 1.976, 1.983, 0.173, 0.855, and 0.833 g/cc, respectively. Variation between planned and IC estimated doses with the tolerance (±5%) for all photon energies using different techniques. Acuros-XB (AXB) showed a slightly higher variation between computed and IC estimated doses using HCP compared to the analytical anisotropic algorithm (AAA). Conclusion
The indigenous heterogeneous phantom can accurately simulate the dosimetric scenario for different algorithms (AXB or AAA) and be also utilized for routine patient-specific QA.Keywords:
Language:
English
Published:
Journal of Biomedical Physics & Engineering, Volume:12 Issue: 3, May-Jun 2022
Pages:
237 to 244
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