A radiotherapy plan optimization system, device and storage medium

Abstract

The invention discloses a radiotherapy plan optimization system, device and storage medium. The radiotherapy plan optimization system includes: a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the following steps are achieved: acquiring an image of the target object, the image Including a first region of interest and a second region of interest; generating initial constraints based on dose targets of the first region of interest and the second region of interest; according to the first region of interest and at least one of the The relationship between the dose targets of the second region of interest determines a drop function; adjusts the initial constraint condition according to the drop function to generate an adjustment constraint condition; optimizes the radiotherapy plan according to the adjustment constraint condition. The technical solution of the embodiment of the present invention realizes the automatic optimization of the radiotherapy plan, and improves the efficiency of generating the radiotherapy plan.

Description

technical field [0001] Embodiments of the present invention relate to the field of medical equipment, and in particular, to a radiotherapy plan optimization system, device, and storage medium. Background technique [0002] Intensity-modulated radiation therapy is an important method in radiotherapy at present. It can protect normal organs as much as possible while increasing the dose to the target area, and reduce the exposure dose to normal organs. [0003] At present, the static field intensity-modulated radiotherapy plan or the rotary intensity-modulated radiotherapy plan need to rely on manual design of parameters such as field angle, target dose and weight factor, organ-at-risk tolerance and weight factor, etc. It takes a lot of time and energy to repeatedly adjust and optimize parameters in the process, and the quality of the treatment plan is directly related to the experience, energy and time invested by the physicist. There is a lack of unified norms and evaluation ...

AI Technical Summary

Problems solved by technology

[0003] At present, the static field intensity-modulated radiotherapy plan or the rotary intensity-modulated radiotherapy plan need to rely on manual design of parameters such as field angle, target dose and weight factor, organ-at-risk tolerance and weight factor, etc. It takes a lot of time and energy to repeatedly adjust and optimize parameters, and the quality of the treatment plan is directly related to the experience, energy and time invested by the physicist. There is a lack of unified norms and evaluation standards.

[0004] Based on the above problems, some treatment planning systems with automatic planning functions have been proposed. One is a prediction method based on historical data. This method requires training data provided by a high-quality planning library to help users determine the dose target of each organization. However, the training model It is very sensitive to training data. Different hospitals and different users may need to train different models. The integration, screening and inspection of high-quality plans, and the completion of the training of the prediction model take a long period of time and the workload is cumbersome.

The other is an automatic optimization method based on a given target condition. Although it does not require a training model, it requires the user to input a large amount of information. It is impossible to predict the rationality of the user input, and the optimization time is too long.

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