Photodynamic Cancer Therapy Using Wavelet Based Monte Carlo Computation of Light Absorption with a Genetic Algorithm

Objective: The method to treating cancer that combines light and light-sensitive drugs to selectively destroy tumour cells without harming healthy tissue is called photodynamic therapy (PDT). It requires accurate data for light dose distribution, generated with scalable algorithms. One of the benchmark approaches involves Monte Carlo (MC) simulations. This gives an accurate assessment of light dose distribution, but is very demanding in computation time, which prevents routine application for treatment planning. Methods: In order to resolve this problem, a design for MC simulation based on the gold standard software in biophotonics was implemented with a large modern wavelet based genetic algorithm search (WGAS). Result: The accuracy of the proposed method was compared to that with the standard optimization method using a realistic skin model. The maximum stop band attenuation of the designed LP, HP, BP and BS filters was assessed using the proposed WGAS algorithm as well as with other methods. Conclusion: In this paper, the proposed methodology employs intermediate wavelets which improve the diversification rate of the charged genetic algorithm search and that leads to significant improvement in design effort efficiency.