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Updated: Jun 25, 2025
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
Published on: June 21, 2024
A back propagation neural network based respiratory motion modelling method
Shan Jiang1, Bowen Li1, Zhiyong Yang1
1School of Mechanical Engineering, Tianjin University, Tianjin, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.
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