2024/01/16 by Keshav Kumar K., K., Keshav Kumar, NVSL Narasimham +3
Decision Sciences · Physics and Astronomy · #Advanced Radiotherapy Techniques #FOS: Mathematics #FOS: Physical sciences #Medical Physics (physics.med-ph) #Optimization and Control (math.OC) #Scheduling and Timetabling Solutions
paper · pdf · doi:10.48550/arxiv.2402.09418
openalex publication_date 2024/01/16 · openalex created_date 2024/02/18 · openalex updated_date 2026/07/28
Radiation Therapy (RT) plays a pivotal role in the treatment of cancer, offering the potential to effectively target and eliminate tumour cells while minimizing harm to surrounding healthy tissues. However, the success of RT heavily depends on meticulous treatment planning that ensures the optimal balance between delivering a sufficiently high dose to the tumour and sparing nearby critical organs. This critical process demands a multidisciplinary approach that combines medical expertise, advanced imaging techniques, and computational tools. Optimization techniques have emerged as indispensable tools in refining RT planning, enabling the precise adjustment of radiation beam arrangements and intensities to achieve treatment objectives while adhering to strict dose constraints. This study delves into the realm of constrained optimization within RT Treatment Planning, employing metaheuristic algorithms to enhance the efficacy of this process. The research focuses on the design and performance comparison of three prominent optimization techniques: Bat Search Optimization (BSO), Bacterial Foraging Algorithm (BFA), and Artificial Bee Colony (ABC). Through systematic evaluation, it is observed that BFA exhibits superior execution time and convergence capabilities in comparison to the other algorithms. This research underscores the crucial importance of RT planning and highlights the imperative need for optimization methodologies to achieve optimal treatment outcomes.