[OA239] Monte Carlo as quality control tool of machine performance and treatment planning system, is it a luxury or a necessity?. (August 2018)

• Record Type: Journal Article
• Authors: Alhamada, Husein; Simon, Stephane; Jourani, Younes; Vandekerkhove, Christophe; Pauly, Nicolas; Dubus, Alain; Reynaert, Nick
• Is Part Of: Physica medica. Volume 52(2018)Supplement 1
• Page Start: 89
• Page End: 90
• Publication Date: 2018-08
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.06.311
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Purpose: During last decades many radiotherapy machines, treatment planning systems (TPS) and complicated treatment techniques (VMAT, IMRT, SBRT) have been developed. Fast dose calculation algorithms are used for radiotherapy, applying many approximations during dose calculation to minimize calculation time. The main objective of this presentation is to present how to implement Monte Carlo in clinical daily quality control of TPS and machine performance. Methods: EGSnrc ⧹ BEAMnrc were used to create a model of the Agility (Elekta) by using the data provided by the manufacturer. Validation of the MC model was done using the methodology of Pena[1]. Comparison of standard treatment plans between MC treatment planning system and measurements was necessary to verify moving parts of the machine. Converting RTplan DICOM files to MC input files and converting MC dose matrix file to a DICOM file were done using the Moderato platform [2]. Impacts of variance reduction techniques, voxel size and simulating volume were well investigated to minimize calculation time. Homemade MATLAB code was used to extract the data from machine log files which was used as input for MC simulation. Results: Validation of MC model was within 2% when comparing to measurement for all PDDs, profiles and output factors of different fields sizes. Good agreement was obtained between TPS algorithm and MC for most of the cases, but small differences were found for small lung lesions, as MC and TPS do not use same cut off energy of secondary electrons. TPS and MC plans using machine log files have a good agreement for all patients. 30 min as max of calculation time and 1% uncertainty for all planning treatment volumes (PTVs) are respected for all patients. The system is currently being extended to use it with other external radiotherapy machines like Clinac® (Varian) and Gamma Knife (Elekta) for double calculation and delivered dose verification. Conclusions: Monte Carlo has been used to validate TPS plans as well as performance of the external radiotherapy machine (Agility (Elekta)). Beside to measurements, MC as independent quality control tool is important for complicated treatment techniques.