A back propagation neural network based respiratory motion modelling method.

Journal: The international journal of medical robotics + computer assisted surgery : MRCAS

PMID: 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.

Authors

Shan Jiang

Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Bowen Li

Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
Zhiyong Yang
Yuhua Li
Zeyang Zhou

Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, People's Republic of China.

Keywords

Algorithms Artificial Intelligence Four-Dimensional Computed Tomography Humans Image Processing, Computer-Assisted Lung Motion Movement Neural Networks, Computer Reproducibility of Results Respiration

External Resources

View on PubMed Access via DOI PubMed (38804195)

A back propagation neural network based respiratory motion modelling method.

Abstract

Authors

Keywords

External Resources

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