Study on the separation method of lung ventilation and lung perfusion signals in electrical impedance tomography based on rime algorithm optimized variational mode decomposition

胸部电阻抗断层扫描（EIT）测量中实时获取肺部通气和灌注信息具有重要的临床意义，故本研究提出了一种胸部EIT中基于霜冰（RIME）算法优化变分模态分解（VMD）的分离肺通气和肺灌注信号的新方法，能从EIT重建图像前的原始电压数据中直接分离肺通气和灌注的相关信号，并可分别进行成像。为验证该方法的有效性，本研究招募了16名健康志愿者，采集其正常呼吸以及吸气屏息两种状态下的EIT数据，同时基于RIME算法，以最小包络熵为适应度函数寻求VMD法的最优参数组合，利用优化后的VMD对EIT全部测量通道的原始数据进行分离，并通过频谱分析识别相关分量重构出新的肺通气与灌注信号。研究结果显示，将分离的正常呼吸状态肺灌注图像与吸气屏息状态的肺灌注图像进行相似性对比，16名志愿者的结构相似性系数（SSIM）平均达到84%左右，相较于传统频域滤波方法（FDFM），肺灌注成像的准确性得到显著提升。本研究提出的方法有望为实时肺部通气和灌注监测提供更精准的技术手段，对推动EIT在呼吸系统疾病诊疗中的临床应用具有重要价值。.; 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.