基于人体结构先验信息的胸部电阻抗成像方法
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摘要
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。
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.
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.
引文
[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 and edge 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.
[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 and edge 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.