Open AccessDevelopment of pelvis phantom for verification of treatment planning system using convolution, fast superposition, and superposition algorithms
Author(s) -
Michael Onoriode Akpochafor,
Chibuzo Bede Madu,
Muhammad Habeebu,
Akintayo Daniel Omojola,
Samuel Olaolu Adeneye,
Moses Adebayo Aweda
Publication year - 2017
Publication title -
journal of clinical sciences
Language(s) - English
Resource type - Journals
eISSN - 2468-6859
pISSN - 2408-7408
DOI - 10.4103/jcls.jcls_78_16
Subject(s) - imaging phantom , algorithm , radiation treatment planning , hounsfield scale , superposition principle , computer science , convolution (computer science) , dosimeter , quality assurance , ionization chamber , nuclear medicine , mathematics , dosimetry , radiation therapy , physics , medicine , computed tomography , artificial intelligence , radiology , ionization , mathematical analysis , artificial neural network , ion , external quality assessment , pathology , quantum mechanics
Background: The cost of commercial pelvis phantom is a burden to the quality assurance in radiotherapy of small and/or low-income radiotherapy centers. That an algorithm is accurate with short treatment time is a prized asset in treatment planning. Objectives: The purpose of this study was to develop a hybrid algorithm that has balance between accuracy and treatment time and design a pelvis phantom for evaluating the accuracy of a linear accelerator monitor unit. Materials and Methods: A pelvis phantom was designed using Plaster of Paris, styrofoam and water with six hollows for inserting materials mimicking different biological tissues, and the ionization chamber. Computed tomography images of the phantom were transferred to the CMS XiO treatment planning system with three different algorithms. Monitor units were obtained with clinical linear accelerator with isocentric setup. The phantom was tested using convolution (C), fast superposition (FSS), and superposition (S) algorithms with respect to an established reference dose of 1 Gy from a large water phantom. Data analysis value was done using GraphPad Prism 5.0. Results: FSS algorithm showed better accuracy than C and S with bone, lung, and solid water inhomogeneous insert. C algorithm was better in terms of treatment time than S. There was no statistically significant difference between the mean doses for all the three algorithms against the reference dose. The maximum percentage deviation was ±4%, which was below ±5% International Commission on Radiation Units and Measurement minimal limit. Conclusion: This algorithm can be employed in the calculation of dose in advance techniques such as intensity-modulated radiation therapy and RapidArc by radiotherapy centers with multiple algorithm system because it is easy to implement. The materials used for the construction of the phantom are very affordable and simple for low-budget radiotherapy centers