A new recursive algorithm for optimization in intensity modulated radiotherapy

• Published in: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143) Vol. 4; pp. 3100 - 3103 vol.4
• Main Authors: Ahmad, S.U., Rahim Leyma, A., Lim Chong Jen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2000
• Subjects: Computer simulation; Distributed computing; Intensity modulation; Inverse problems; Iterative algorithms; Kalman filters; Neoplasms; Optical modulation; Steady-state; Stochastic processes
• ISBN: 9780780364653; 0780364651
• ISSN: 1094-687X; 1558-4615
• DOI: 10.1109/IEMBS.2000.901538

Intensity Modulated Radiotherapy (IMRT) offers considerable advantages over traditional radiotherapy systems as highly conformal dose distribution can be achieved by this technique. But the dose optimization problems of IMRT are, however, formulated as ill-posed inverse problem. It concerns the delivery of proper radiation dose to a tumor, at the same time it maintains the minimum dose level at radiosensitive parts of the treatment regime to avoid irreparable radiation damage. Hence, there exist 2 mutually contradictory issues in the dose optimization objective that limit the success of different iterative optimization algorithms. Here, the authors have formulated a state-space model for the dose-beam relationship in IMRT. They propose a 2-D steady state Kalman filter-type algorithm to determine intensity-modulated beams, which yields an optimized dose distribution. The evaluation of this new approach is done with extensive computer simulations using 2-D body sections with different irregular target shapes and radiosensitive organs. With pre-selected number of equispaced beams, field size and ignoring scattering from regional beam sources, the authors allowed the program to obtain an optimum beam intensity solution that would most closely match the clinically prescribed dose-values. The simulation results showed that this new approach has a great potential to be used as an optimization tool in radiotherapy treatment planning.