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Simulation on human respiratory motion dynamics and platform construction

Bao Yudong, Li Xu, Wei Wen, Qu Shengquan, Zhan Yang

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 4

736--750

Bronchoscopy has a crucial role in the current treatment of lung diseases, and it is typical of interventional medical instruments led by manual intervention. The scientific study of bronchoscopy is now of primary importance in eliminating problems associated with manual intervention by scientific means. However, for its intervention environment, the trachea is often treated statically, without considering the effect of tracheal deformation on bronchoscopic intervention during respiratory motion. Therefore its findings can deviate from practical application. Thus, studying kinetic problems in respiratory motion is of great importance. This paper developed a mathematical model of mechanical properties of respiratory motion to express respiratory force from the perspective of dynamics of respiratory motion. The dynamical model was solved using MATLAB. Then, a finite element model of respiratory motion was built using Mimics, and the results of respiratory force solution were used as the load of model for dynamics simulation in ABAQUS. Then, a human–computer interaction platform was designed in MATLAB APP Designer to realize parametric calculation and fitting of respiratory force, and a personalized human respiratory motion dynamics simulation was completed in conjunction with ABAQUS. Finally, experimental validation of the interactive platform was performed using pulmonary function test data from three patients. Validation analysis by respiration striving solution, kinetic simulation and experiment found that Dynamical model and simulation results can be better adapted to the individualized study of human respiratory motion dynamics.

Modelling the effects of lung cancer motion due to respiration

Adamczyk M., Adamczyk S., Piotrowski T.

Nukleonika

2018

Vol. 63, No. 4

95--103

Background and objectives: To justify the concept of validating conformal versus intensity-modulated approach in the treatment of non-small cell lung cancer (NSCLC). Materials and methods: For 10 patients representative of the spectrum of tumour sizes and locations, two plans were prepared: one with three-dimensional conformal radiation therapy (3DCRT) technique and the other with intensity-modulated radiation therapy (IMRT) technique. Preliminary measurements were performed in static conditions. For each of the field angles considered, the motion kernel was generated to simulate tumour motion trajectories, with the largest amplitude in the cranio-caudal direction of 4, 6, and 8 mm. The measurement results determined the agreement between the planned and measured doses. Results: No statistically significant differences were found between the motion patterns, with the smallest amplitudes for clinical target volume in 3DCRT. For IMRT, the significant differences between 0 mm vs. 6 mm and 0 mm vs. 8 mm amplitudes were found. The motion impact on delivered vs. planned doses had less effect on the oesophagus in 3DCRT compared to that in IMRT. The observed differences were comparable for the heart. Interpretation and conclusions: For maximal amplitudes below 4 mm, the disagreement between planned and delivered doses can be neglected. However, the amplitudes above 5 mm and 7 mm lead to significant changes in IMRT and 3DCRT dose distribution, respectively.

Intelligent sensing and monitoring : respiratory motion prediction for tumor following radiotherapy

Ichiji K., Homma N., Sakai M., Bukovsky I., Zhang X., Osanai M., Abe M., Sugita N., Yoshizawa M.

Journal of Artificial Intelligence and Soft Computing Research

2012

Vol. 2, No. 4

331--342

This paper presents a medical application of the intelligent sensing and monitoring, a new lung tumor motion prediction method for tumor following radiation therapy. An essential core of the method is accurate estimation of complex fluctuation of time-varying periodical nature of lung tumor motion. Such estimation is achieved by using a novel multiple time-varying seasonal autoregressive (TVSAR) model in which several windows of different time-lengths are used to calculate correlation based fluctuation of periodic nature in the motion. The proposed method provides the prediction as a combination of those based on different window lengths. Multiple regression (MR), multilayer perceptron (MLP) and support vector regression (SVR) are used to combine and the prediction performances are evaluated by using clinical lung tumor motion. The proposed methods with the combined predictions showed high accurate prediction and are superior to the single different predictions. The average errors of MR, MLP, and SVR were 0.8455,0.8507, and 0.7530 mm at 0.5 s ahead, respectively. The results are clinically sufficient and thus clearly demonstrate that the proposed TVSAR with an appropriate combination method is useful for improving the prediction performance.