BEAM ANGLE OPTIMIZATION IN RADIOTHERAPY USING SIMULATED ANNEALING METHODS

• Main Author: RATNASARI (NIM : 10214097), RINA
• Format: Final Project
• Language: Indonesia
• Subjects: Fisika
• Online Access: https://digilib.itb.ac.id/gdl/view/30504
• Institution: Institut Teknologi Bandung

Radiotherapy is one of treatment methods used to damage cancer cells in the patient’s body by utilizing ionizing radiation energy without damaging the surrounding normal structure. Before doing the irradiation, an optimal Radiotherapy Treatment Planning (RTP) is required to achieve radiotherapy goals. When the patient is irradiated, there is a possibility for the organ at risk (OAR) around the target volume getting a high dose. Therefore, optimal RTP parameters (amount and angle of beam) are required to obtain the optimal RTP where the target volume receiving the maximum dose and the OARs receiving the minimum dose. The optimal RTP selection can be obtained through optimization process. Nowadays there have been many optimization methods developed, one of them is beam angle optimization. In this research, a beam angle optimization process with Simulated Annealing method will be done on prostate cancer case for 5, 7, and 9 beam angle variations using MATLAB software. Based on the beam angle optimization that has been done using initial temperature parameters of 1000 and cold temperature of 1, it is obtained a new beam angle configurations for prostate cancer case, those are 50.3º, 115.7º, 247.6º, 278.1º, 358.2º for RTP with 5 beam angle; 38.0º, 97.1º, 135.1º, 181.5º, 252.6º, 294.8º, 345.0º for RTP with 7 beam angle; and 2.1º, 75.4º, 118.5º, 168.6º, 229.6º, 244.9º, 271.5º, 283.1º, 305.2º for RTP with 9 beam angle, which have smaller objective function value than it was before optimization. After obtaining the optimal configuration of beam angle, the RTP before and after optimization are being analyzed through the dose color wash and cumulative Dose Volume Histogram (DVH) curve. In the result of beam angle optimization, the dose that received by the OARs around the target volume is smaller than before the optimization process was performed.