基于人体结构先验信息的胸部电阻抗成像方法

电阻抗成像(EIT)技术对于人体胸腔病理变化及肺部检测具有重要的临床价值。由于胸部轮廓具有特异性,传统模型成像方法误差较大。提出一种基于人体结构先验信息的胸部电阻抗成像方法,通过对CT图片进行图像处理来提取胸部及肺部轮廓,为正问题和逆问题提供图像边界的先验信息,同时基于边界先验信息提出一种有效的图像逆问题剖分方法,使重建图像形状更接近真实情况,改善成像效果。为进行有效性验证,从某医院CT数据库中选取30张肺部健康的人体CT图像,对于所提出的方法与两种传统模型成像方法(基于椭圆形模型和基于圆形模型的成像方法),分别就其肺部区域比例(LRR)与真实值以及所产生的相对误差进行统计学对比分析。结果表明,所提出方法的LRR与真实值之间无显著性差异,其相对误差(3.71%±1.77%)显著小于基于椭圆形模型(10.29%±3.30%)和基于圆形模型(12.74%±2.87%)这两种成像方法(P<0.05),能有效提高成像质量。

Chest Electrical Impedance Tomography Method Based on Priori Information of Human Body Structure

Electrical impedance tomography (EIT) technique has important clinical values in human thoracic pathological changes and lung detection. Due to the specificity of the chest contour, the reconstructed images based on traditional model imaging methods often have large errors. In this paper, we proposed a chest electrical impedance tomography method based on prior information of human body structure. The contours of the chest and lungs were extracted through the image processing of CT images, which provided prior information for forward and inverse problems of EIT. At the same time, an efficient subdivision method for inverse problem was proposed, which makes the shapes of reconstructed images closer to the real one. As a result, the quality of reconstruction was improved. In order to verify the effectiveness of the method, thirty samples of lung CT images for healthy human were selected from a hospital CT database. For the proposed method and two traditional methods, namely elliptical model imaging method and circular model imaging method, the statistical analysis of the lung region ratio (LRR) for the three methods were conducted. The results showed that there was no significant difference between the real LRR and the computed LRR based on the proposed method. The relative errors between the computed LRR based on proposed method and the real one was 3.71%±1.77%, which was much smaller than the elliptical model imaging method (10.29%±3.30%) and the circular model imaging method (12.74%±2.87%). The statistical significance was P<0.05. In conclusion, the proposed method could effectively improve the imaging quality.