%0 Journal Article
%T A back propagation neural network based respiratory motion modelling method.
%A Jiang S
%A Li B
%A Yang Z
%A Li Y
%A Zhou Z
%J Int J Med Robot
%V 20
%N 3
%D 2024 Jun
%M 38804195
%F 2.483
%R 10.1002/rcs.2647
%X <B>BACKGROUND: </B>This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases.<BR><B>METHODS: </B>Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement.<BR><B>RESULTS: </B>The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm.<BR><B>CONCLUSIONS: </B>The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.