Prediction and Analysis of Respiratory Circulation System in Radiotherapy Patients by Four-Dimensional Computed Tomography Image Technology

Abstract

Usually, radiotherapy not only acts on the parts that need to be treated, but also radiates on the normal parts of the human body, resulting in damage to the normal parts. Recently, some scholars have used the fourdimensional CT image technology to monitor and adjust the radiotherapy process in real time, which can reduce the damage to the normal parts. Therefore, it is a very meaningful subject to apply the real-time monitoring technology to the radiotherapy process of the respiratory circulation system. In order to reduce the damage of related organs in the respiratory circulation system during radiotherapy, this paper optimizes the particle swarm optimization (PSO) algorithm and combines it with artificial neural network (ANN) technology to predict and analyze the respiratory circulation system of human body. The results show that in the analysis of CT image results, there is no significant difference in respiratory flow velocity between the left lung and the right lung from three-dimensional point of view (P > 0.05). Different algorithms are used to analyze the respiratory circulation system, and it is found that the model constructed in this paper can effectively reduce the probability of BP-NN (Back Propagation-Neural Network) falling into the local optimum, and the error is reduced by 25%, while the effect of the original PSO algorithm is inferior to that of the model constructed. Therefore, through this study, it is found that the PSO algorithm can be used in real-time monitoring of the radiotherapy process, and its prediction accuracy has been significantly improved, which achieves the expected effect, and provides experimental basis for the later clinical radiotherapy of respiratory circulation system.