Study on the separation method of lung ventilation and lung perfusion signals in electrical impedance tomography based on rime algorithm optimized variational mode decomposition.
10.7507/1001-5515.202410012
- Author:
Guobin GAO
1
;
Kun LI
1
;
Junyao LI
2
;
Mingxu ZHU
2
;
Yu WANG
3
;
Xiaoheng YAN
1
;
Xuetao SHI
2
Author Information
1. Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning 125105, P. R. China.
2. Department of Biomedical Engineering, Air Force Medical University, Xi'an 710032, P. R. China.
3. Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, P. R. China.
- Publication Type:Journal Article
- Keywords:
Blood perfusion;
Electrical impedance tomography;
Pulmonary ventilation;
Rime algorithm;
Variational mode decomposition
- MeSH:
Humans;
Electric Impedance;
Algorithms;
Tomography/methods*;
Pulmonary Ventilation/physiology*;
Lung/diagnostic imaging*;
Image Processing, Computer-Assisted/methods*;
Adult
- From:
Journal of Biomedical Engineering
2025;42(2):228-236
- CountryChina
- Language:Chinese
-
Abstract:
Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.
