Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstr...Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.展开更多
Medically, electrical impedance tomography （EIT） is a relatively inexpensive, safe, non-invasive and portable technique compared with computerized tomography （CT） and magnetic resonance imaging （MRI）. In this pa...Medically, electrical impedance tomography （EIT） is a relatively inexpensive, safe, non-invasive and portable technique compared with computerized tomography （CT） and magnetic resonance imaging （MRI）. In this paper, EIT_TJU_ II system is developed including both the data collection system and image reconstruction algo- rithm. The testing approach of the system performance, including spatial resolution and sensitivity, is described through brine tank experiments. The images of the thorax physical model verify that the system can reconstruct the interior resistivity distribution. Finally, the lung ventilation functional monitoring in vivo is realized by EIT, and the visualized images indicate that the configuration and performance of EIT_TJU_ II system are feasible and EIT is a promising technique in clinical monitoring application.展开更多
In electrical impedance tomography (EIT) an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. Several...In electrical impedance tomography (EIT) an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. Several difficulties have been identified in EIT, where the main problem is the low spatial resolution. This paper presents a fining mesh method based on finite element method (FEM), by fining the sensitive element, the most actual signal is obtained in certain electrode number. Newton-Raphson reconstruction algorithm improves the spatial solution of image. The advantages of this method are the improvement of spatial resolution and ease of implementation.展开更多
EIT （electrical impedance tomography） problem should be represented by a group of partial differential equation, in numerical calculation： the nonlinear problem should be linearization approximately, and then linea...EIT （electrical impedance tomography） problem should be represented by a group of partial differential equation, in numerical calculation： the nonlinear problem should be linearization approximately, and then linear equations set is obtained, so EIT image reconstruct problem should be considered as a classical ill-posed, ill-conditioned, linear inverse problem. Its biggest problem is the number of unknown is much more than the number of the equations, this result in the low imaging quality. Especially, it can not imaging in center area. For this problem, we induce the CS technique into EIT image reconstruction algorithm. The main contributions in this paper are： firstly, built up the relationship between CS and EIT definitely; secondly, sparse reconstruction is a critical step in CS, built up a general sparse regularization model based on EIT; finally, gives out some EIT imaging models based on sparse regularization method. For different scenarios, compared with traditional Tikhonov regularization （smooth regularization） method, sparse reconstruction method is not only better at anti-noise, and imaging in center area, but also faster and better resolution.展开更多
Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image recons...Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.展开更多
Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution inform...Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution information of the human tissue and organ,an image reconstruction method of electrical impedance tomography based on pulmonary prior information is proposed using the conjugate gradient method.Simulation results show that the uniformity index of sensitivity distribution of the pulmonary model is 15.568,which is significantly reduced compared with 34.218 based on the round field.The proposed algorithm improves the uniformity of the sensing field,the image resolution of the conductivity distribution of pulmonary tissue and the quality of the reconstruction image based on pulmonary prior information.展开更多
With the recent promotion of clinical applications of electrical impedance tomography(EIT)technology,more scholars have begun studying EIT technology.Although the principle of EIT technology seems simple,EIT image rec...With the recent promotion of clinical applications of electrical impedance tomography(EIT)technology,more scholars have begun studying EIT technology.Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field.What’s more,the EIT reconstruction algorithm requires further improvements in robustness,clarity,etc.The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology;thus,more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems.In this paper,we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.展开更多
文摘Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.
基金Supported by National Natural Science Foundation of China (No.60820106002, No.60532020)Tianjin Natural Science Foundation (No.08JCYBJC03500).
文摘Medically, electrical impedance tomography （EIT） is a relatively inexpensive, safe, non-invasive and portable technique compared with computerized tomography （CT） and magnetic resonance imaging （MRI）. In this paper, EIT_TJU_ II system is developed including both the data collection system and image reconstruction algo- rithm. The testing approach of the system performance, including spatial resolution and sensitivity, is described through brine tank experiments. The images of the thorax physical model verify that the system can reconstruct the interior resistivity distribution. Finally, the lung ventilation functional monitoring in vivo is realized by EIT, and the visualized images indicate that the configuration and performance of EIT_TJU_ II system are feasible and EIT is a promising technique in clinical monitoring application.
文摘In electrical impedance tomography (EIT) an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. Several difficulties have been identified in EIT, where the main problem is the low spatial resolution. This paper presents a fining mesh method based on finite element method (FEM), by fining the sensitive element, the most actual signal is obtained in certain electrode number. Newton-Raphson reconstruction algorithm improves the spatial solution of image. The advantages of this method are the improvement of spatial resolution and ease of implementation.
文摘针对LSQR(least square QR-factorization)算法在求解电阻抗层析成像(electrical impedance tomography,EIT)逆问题时,由于矩阵维数高、计算量大而导致重建速度较慢的问题,提出基于小波多分辨分析的LSQR算法(wavelet multi-resolution based least square QR-factorization,WALSQR)。该方法将EIT的图像重建过程投影到低维的尺度空间进行,通过提取有效信息减少数据计算量,明显提高了图像重建速度。同时由于去除了噪声和冗余信息,保证了成像质量。本文将SALSQR方法分别应用于二维、三维EIT成像实验,证明其有效性,并为三维动态EIT图像重建算法的研究奠定了基础。
基金This work was supported by Chinese Postdoctoral Science Foundation （2012M512098）, Science and Technology Research Project of Shaanxi Province （2012K13-02-10）, the National Science ＆ Technology Pillar Program （2011BAI08B13 and 2012BAI20B02）, Military Program （AWS 11 C010-8）.
文摘EIT （electrical impedance tomography） problem should be represented by a group of partial differential equation, in numerical calculation： the nonlinear problem should be linearization approximately, and then linear equations set is obtained, so EIT image reconstruct problem should be considered as a classical ill-posed, ill-conditioned, linear inverse problem. Its biggest problem is the number of unknown is much more than the number of the equations, this result in the low imaging quality. Especially, it can not imaging in center area. For this problem, we induce the CS technique into EIT image reconstruction algorithm. The main contributions in this paper are： firstly, built up the relationship between CS and EIT definitely; secondly, sparse reconstruction is a critical step in CS, built up a general sparse regularization model based on EIT; finally, gives out some EIT imaging models based on sparse regularization method. For different scenarios, compared with traditional Tikhonov regularization （smooth regularization） method, sparse reconstruction method is not only better at anti-noise, and imaging in center area, but also faster and better resolution.
基金the National Natural Science Foundation of China(No.61371017)。
文摘Electrical impedance tomography(EIT)image reconstruction is a non-linear problem.In general,finite element model is the critical basis of EIT image reconstruction.A 3D human thorax modeling method for EIT image reconstruction is proposed herein to improve the accuracy and reduce the complexity of existing finite element modeling methods.The contours of human thorax and lungs are extracted from the layers of magnetic resonance imaging(MRI)images by an optimized Otsu’s method for the construction of the 3D human thorax model including the lung models.Furthermore,the GMSH tool is used for finite element subdivision to generate the 3D finite element model of human thorax.The proposed modeling method is fast and accurate,and it is universal for different types of MRI images.The effectiveness of the proposed method is validated by extensive numerical simulation in MATLAB.The results show that the individually oriented 3D finite element model can improve the reconstruction quality of the EIT images more effectively than the cylindrical model,the 2.5D model and other human chest models.
基金supported by the National Key Technology R&D Program (Grant No.2006BAIO3A00)the Natural Science Foundation of Tianjin Municipal Science and Technology Commission (No.08JCYBJC03500).
文摘Using a CT scan of the pulmonary tissue,a human pulmonary model is established combined with the structure property of the human lung tissue using the software COMSOL.Combined with the conductivity contribution information of the human tissue and organ,an image reconstruction method of electrical impedance tomography based on pulmonary prior information is proposed using the conjugate gradient method.Simulation results show that the uniformity index of sensitivity distribution of the pulmonary model is 15.568,which is significantly reduced compared with 34.218 based on the round field.The proposed algorithm improves the uniformity of the sensing field,the image resolution of the conductivity distribution of pulmonary tissue and the quality of the reconstruction image based on pulmonary prior information.
基金the National Natural Science Foundation of China(No.61371017)。
文摘With the recent promotion of clinical applications of electrical impedance tomography(EIT)technology,more scholars have begun studying EIT technology.Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field.What’s more,the EIT reconstruction algorithm requires further improvements in robustness,clarity,etc.The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology;thus,more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems.In this paper,we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.
