A back propagation neural network based respiratory motion modelling method.
Int J Med Robot
; 20(3): e2647, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38804195
ABSTRACT
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.Palabras clave
Texto completo:
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Base de datos:
MEDLINE
Asunto principal:
Respiración
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Algoritmos
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Redes Neurales de la Computación
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Tomografía Computarizada Cuatridimensional
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Pulmón
Idioma:
En
Revista:
Int J Med Robot
Año:
2024
Tipo del documento:
Article