{Reference Type}: Journal Article
{Title}: A back propagation neural network based respiratory motion modelling method.
{Author}: Jiang S;Li B;Yang Z;Li Y;Zhou Z;
{Journal}: Int J Med Robot
{Volume}: 20
{Issue}: 3
{Year}: 2024 Jun
{Factor}: 2.483
{DOI}: 10.1002/rcs.2647
{Abstract}: <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.