Multiple-objective optimization method and system capable of optimizing radiotherapy beam intensity distribution

Abstract

The invention discloses a multipurpose optimal method for optimizing the distribution of radiation therapy beam intensity and a system thereof. The method includes: the establishment of an optimization model, which is used for transferring requirements of plan designers and clinical limits into an optimal mathematical model; and multipurpose optimization of improved rapid nondominated multipurpose genetic algorithm, which is used for optimizing strength scattergram of each radiation beam. The system comprises a data input module, an optimization model establishing module, a multipurpose optimal module, and a data output module. The invention is characterized by accurate establishment of optimization problem mathematic models and high solving speed, and is also characterized in that a plurality of noinferior solutions can be got after multipurpose one-time optimization, therefore the plan designers can choose optimum solutions to meet requirements from all the solutions.

Description

technical field [0001] The present invention relates to a multi-objective optimization method and system for optimizing the intensity distribution of radiotherapy beams. Background technique [0002] Intensity modulated radiation therapy (IMRT) is a hot spot in the development of radiation therapy technology in the 21st century. In intensity-modulated radiotherapy, by adjusting the intensity distribution of small beams on the beam profile, the high-dose area covers the target area, and at the same time, the organs at risk or other normal tissues near the target area are well protected. The element field intensity distribution on the beam profile needs to be given by the inverse treatment plan. Inverse treatment planning means: the plan designer gives the required dose of the target area and the dose limit of the surrounding normal tissue, establishes the corresponding objective function and constraint conditions, and uses the optimization algorithm to adjust the irradiation...

AI Technical Summary

Problems solved by technology

Since the treatment plan usually contains multiple objective functions and conflicts with each other, the selection of weights still depends on subjective factors. This is a process of trial and error. This method is not only cumbersome, but also requires considerable clinical Experience, it is usually difficult to make appropriate judgments and choices, and it is often difficult to obtain a satisfactory solution after optimization after the weight is determined. Even if it can be obtained, a satisfactory solution that satisfies a good dose distribution, but when the intensity distribution is discretized , may bring a lot of trouble to sub-wilding, and even need to re-optimize the beam degree

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