|
|
Chest Electrical Impedance Tomography Method Based on Priori Information of Human Body Structure |
Wang Qi1,2, Chen Xiaojing1,2, Wang Jianming1,2*, Li Xiuyan1,2, Duan Xiaojie1,2, Wang Huaxiang3 |
1School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387,China; 2School Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China; 3School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China |
|
|
Abstract 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.
|
Received: 07 November 2017
|
|
|
|
|
[1] 董秀珍. 生物电阻抗成像研究的现状与挑战[J]. 中国生物医学工程学报,2008,27(5):641-643. [2] 罗辞勇,陈民铀,王平,等. 电阻抗成像交叉测量模式的抗噪声性能研究[J]. 仪器仪表学报,2009,30(1):14-19. [3] Bodenstein M,David M,Markstaller K,et al. Principles of electrical impedance tomography and its clinical application [J]. Crit Care Med,2009,37(2):713-724. [4] Freichs,Weiler. EIT the next game level [J]. Crit Care Med,2012,40(3):1015-1016. [5] Nguyen DT,Jin C,Thiagalingam A,et al. A review on electrical impedance tomography for pulmonary pefusion imaging [J]. Physiol Meas,2012,33(5):695-706. [6] 徐管鑫,王平,何为. 实时电阻抗成像系统及实验研究[J]. 仪器仪表学报,2005,26(1):886-891. [7] 贺建林. 基于几何边界的EIT网格自适应剖分方法的研究[D]. 西安:中国人民解放军第四军医大学,2009. [8] 王化祥,胡理,赵波. 基于自适应网格剖分的EIT图像重建算法[C]//中国生物医学工程进展——2007中国生物医学工程联合学术年会论文集:下册. 西安:西安交通大学出版社,2007:863-867. [9] 侯雪. 基于comsol的肺部电阻抗断层成像仿真研究[D]. 天津:天津大学,2012. [10] 范文茹,王化祥,马雪翠. 基于先验信息的肺部电阻抗成像算法[J]. 中国生物医学工程学报,2009,28(5):680-685. [11] Saka B,Yimaz A. Elliptic cylinder geometry for distinguishability analysis in impedance tomography [J]. IEEE Transformation to Biomedical Engineering,2004,51(1):126-132. [12] 严佩敏,曹永香. 基于椭圆几何模型的胸部电阻抗成像[J]. 电子测量技术,2016,39(6):81-84. [13] Jain H,Isaacson D,Edic P,et al. Electrical impedance tomography of complex conductivity distribution with noncircular boundary [J]. IEEE Transformation to Biomedical Engineering,1997,44(11):1051-1060. [14] Perona P,Malik J. Scale-space andedge detection using anisotropic diffusion [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1990,12(7):629-639. [15] 陈欣. 基于医学图像的Snake轮廓提取算法研究[D]. 长沙:国防科学技术大学,2004. [16] 阳天舒,李梅,信荟敏,等. 基于形态学的自适应阈值分割算法[J]. 电子设计工程,2015,23(13):102-104. [17] 杨丹,游磊,张小洪,等. 基于区域生长的鱼眼圈图像轮廓提取算法[J]. 计算机工程,2010,36(8):217-219. [18] 范文茹. 生物电阻抗成像技术研究[D]. 天津:天津大学,2010. [19] Lionheart WR. EIT reconstruction algorithms: pitfalls,challenges and recent developments [J]. Physiological Measurement,2004,25(1):125-142. [20] 范文茹,王化祥,郝魁红,等. 基于TwISL-TV正则化的肺萎缩电阻抗成像仿真研究[J]. 中国生物医学工程学报,2013,32(1):1-6. [21] 殷苏民,朱锦萍,王祖声,等. 基于顶帽变换和最大类间方差法的图像分割方法研究[J]. 科学技术与工程,2014,14(7):1671-1815. [22] 侯雪,王超,刘凖. 基于共轭梯度算法的EIT仿真[J]. 长春理工大学学报,2012,35(4):167-170. [23] 赵正松,潘登登. 基于两独立样本和配对样本T检验分析出租车行业运行规律[J]. 交通建设与管理,2013(8):86-87. [24] Boyle A,Adler A,Lionheart WRB. Shape deformation in two dimensional electrical impedance tomography [J]. IEEE Transformation on Medical Imaging,2012,31(12):2185-2193. [25] Boyle A,Adler A. The impact of electrode area, contact impedance and boundary shape on EIT images [J]. Physiological Measurement,2011,32(7):745-754. |
[1] |
Li Zhangyong, Liu Zhaoyu, Ran Peng, Xiang Shangzhi, Ma Chengqun, Wang Wei. Construction and Simulation of Three-Layer EIT Model in Gastric[J]. Chinese Journal of Biomedical Engineering, 2019, 38(5): 590-598. |
[2] |
Chen Xiaoyan, Chu Mengli, Chang Xiaomin, Zhang Xiaojie. 3D EIT Model Construction Based on Lung and Image Reconstruction Research[J]. Chinese Journal of Biomedical Engineering, 2017, 36(5): 622-626. |
|
|
|
|