A back propagation neural network based respiratory motion modelling method.
Int J Med Robot
; 20(3): e2647, 2024 Jun.
Article
de 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.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Respiration
/
Algorithmes
/
/
Tomodensitométrie 4D
/
Poumon
Limites:
Humans
Langue:
En
Journal:
Int J Med Robot
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine