The Experts below are selected from a list of 68298 Experts worldwide ranked by ideXlab platform
Ozlem Birgul - One of the best experts on this subject based on the ideXlab platform.
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experimental results for 2d magnetic resonance electrical impedance tomography mr eit using magnetic flux density in one direction
Physics in Medicine and Biology, 2003Co-Authors: Murat B Eyuboglu, Ozlem Birgul, Ziya Y IderAbstract:Magnetic resonance electrical impedance tomography (MR-EIT) is an emerging imaging technique that reconstructs Conductivity images using magnetic flux density measurements acquired employing MRI together with conventional EIT measurements. In this study, experimental MR-EIT images from phantoms with conducting and insulator objects are presented. The technique is implemented using the 0.15 T Middle East Technical University MRI system. The dc current method used in magnetic resonance current density imaging is adopted. A reconstruction algorithm based on the sensitivity matrix relation between Conductivity and only one component of magnetic flux Distribution is used. Therefore, the requirement for object rotation is eliminated. Once the relative Conductivity Distribution is found, it is scaled using the peripheral voltage measurements to obtain the absolute Conductivity Distribution. Images of several insulator and conductor objects in saline filled phantoms are reconstructed. The L2 norm of relative error in Conductivity values is found to be 13%, 17% and 14% for three different Conductivity Distributions.
Ziya Y Ider - One of the best experts on this subject based on the ideXlab platform.
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experimental results for 2d magnetic resonance electrical impedance tomography mr eit using magnetic flux density in one direction
Physics in Medicine and Biology, 2003Co-Authors: Murat B Eyuboglu, Ozlem Birgul, Ziya Y IderAbstract:Magnetic resonance electrical impedance tomography (MR-EIT) is an emerging imaging technique that reconstructs Conductivity images using magnetic flux density measurements acquired employing MRI together with conventional EIT measurements. In this study, experimental MR-EIT images from phantoms with conducting and insulator objects are presented. The technique is implemented using the 0.15 T Middle East Technical University MRI system. The dc current method used in magnetic resonance current density imaging is adopted. A reconstruction algorithm based on the sensitivity matrix relation between Conductivity and only one component of magnetic flux Distribution is used. Therefore, the requirement for object rotation is eliminated. Once the relative Conductivity Distribution is found, it is scaled using the peripheral voltage measurements to obtain the absolute Conductivity Distribution. Images of several insulator and conductor objects in saline filled phantoms are reconstructed. The L2 norm of relative error in Conductivity values is found to be 13%, 17% and 14% for three different Conductivity Distributions.
Eung Je Woo - One of the best experts on this subject based on the ideXlab platform.
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a posteriori error estimate and convergence analysis for Conductivity image reconstruction in mreit
Siam Journal on Applied Mathematics, 2010Co-Authors: Jijun Liu, Jin Keun Seo, Eung Je WooAbstract:Magnetic resonance electrical impedance tomography (MREIT) takes advantage of internal information to solve its nonlinear inverse problem of recovering a Conductivity Distribution inside an imaging object. When we inject current into the imaging object, there occurs a Distribution of internal magnetic flux density $\mathbf{B}=(B_x,B_y,B_z)$. In MREIT we utilize a magnetic resonance imaging scanner with its main magnetic field in the z direction to acquire $B_z$ data. The harmonic $B_z$ algorithm was invented in 2001 to reconstruct cross-sectional Conductivity images from $B_z$ data sets subject to multiple injection currents. Utilizing internal $B_z$ data, it overcomes the inherent ill posedness in electrical impedance tomography. We can set up the inverse problem in MREIT as a coefficient identification problem of finding $\sigma$ appearing in $\nabla\cdot(\sigma\nabla u)=0$ from acquired data of the z component of $\nabla\times(\sigma\nabla u)$. The harmonic $B_z$ algorithm has shown an excellent perfor...
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factorization method and its physical justification in frequency difference electrical impedance tomography
IEEE Transactions on Medical Imaging, 2010Co-Authors: Bastian Harrach, Jin Keun Seo, Eung Je WooAbstract:Time-difference electrical impedance tomography (tdEIT) requires two data sets measured at two different times. The difference between them is utilized to produce images of time-dependent changes in a complex Conductivity Distribution inside the human body. Frequency-difference EIT (fdEIT) was proposed to image frequency-dependent changes of a complex Conductivity Distribution. It has potential applications in tumor and stroke imaging since it can visualize an anomaly without requiring any time-reference data obtained in the absence of an anomaly. In this paper, we provide a rigorous analysis for the detectability of an anomaly based on a constructive and quantitative physical correlation between a measured fdEIT data set and an anomaly. From this, we propose a new noniterative frequency-difference anomaly detection method called the factorization method (FM) and elaborate its physical justification. To demonstrate its practical applicability, we performed fdEIT phantom imaging experiments using a multifrequency EIT system. Applying the FM to measured frequency-difference boundary voltage data sets, we could quantitatively evaluate indicator functions inside the imaging domain, of which values at each position reveal presence or absence of an anomaly. We found that the FM successfully localizes anomalies inside an imaging domain with a frequency-dependent complex Conductivity Distribution. We propose the new FM as an anomaly detection algorithm in fdEIT for potential applications in tumor and stroke imaging.
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magnetic resonance electrical impedance tomography mreit for high resolution Conductivity imaging
Physiological Measurement, 2008Co-Authors: Eung Je Woo, Jin Keun SeoAbstract:Cross-sectional imaging of an electrical Conductivity Distribution inside the human body has been an active research goal in impedance imaging. By injecting current into an electrically conducting object through surface electrodes, we induce current density and voltage Distributions. Based on the fact that these are determined by the Conductivity Distribution as well as the geometry of the object and the adopted electrode configuration, electrical impedance tomography (EIT) reconstructs cross-sectional Conductivity images using measured current-voltage data on the surface. Unfortunately, there exist inherent technical difficulties in EIT. First, the relationship between the boundary current-voltage data and the internal Conductivity Distribution bears a nonlinearity and low sensitivity, and hence the inverse problem of recovering the Conductivity Distribution is ill posed. Second, it is difficult to obtain accurate information on the boundary geometry and electrode positions in practice, and the inverse problem is sensitive to these modeling errors as well as measurement artifacts and noise. These result in EIT images with a poor spatial resolution. In order to produce high-resolution Conductivity images, magnetic resonance electrical impedance tomography (MREIT) has been lately developed. Noting that injection current produces a magnetic as well as electric field inside the imaging object, we can measure the induced internal magnetic flux density data using an MRI scanner. Utilization of the internal magnetic flux density is the key idea of MREIT to overcome the technical difficulties in EIT. Following original ideas on MREIT in early 1990s, there has been a rapid progress in its theory, algorithm and experimental techniques. The technique has now advanced to the stage of human experiments. Though it is still a few steps away from routine clinical use, its potential is high as a new impedance imaging modality providing Conductivity images with a spatial resolution of a few millimeters or less. This paper reviews MREIT from the basics to the most recent research outcomes. Focusing on measurement techniques and experimental methods rather than mathematical issues, we summarize what has been done and what needs to be done. Suggestions for future research directions, possible applications in biomedicine, biology, chemistry and material science are discussed.
Murat B Eyuboglu - One of the best experts on this subject based on the ideXlab platform.
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experimental results for 2d magnetic resonance electrical impedance tomography mr eit using magnetic flux density in one direction
Physics in Medicine and Biology, 2003Co-Authors: Murat B Eyuboglu, Ozlem Birgul, Ziya Y IderAbstract:Magnetic resonance electrical impedance tomography (MR-EIT) is an emerging imaging technique that reconstructs Conductivity images using magnetic flux density measurements acquired employing MRI together with conventional EIT measurements. In this study, experimental MR-EIT images from phantoms with conducting and insulator objects are presented. The technique is implemented using the 0.15 T Middle East Technical University MRI system. The dc current method used in magnetic resonance current density imaging is adopted. A reconstruction algorithm based on the sensitivity matrix relation between Conductivity and only one component of magnetic flux Distribution is used. Therefore, the requirement for object rotation is eliminated. Once the relative Conductivity Distribution is found, it is scaled using the peripheral voltage measurements to obtain the absolute Conductivity Distribution. Images of several insulator and conductor objects in saline filled phantoms are reconstructed. The L2 norm of relative error in Conductivity values is found to be 13%, 17% and 14% for three different Conductivity Distributions.
Jin Keun Seo - One of the best experts on this subject based on the ideXlab platform.
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A mathematical and numerical framework for ultrasonically-induced Lorentz force electrical impedance tomography
Journal de Mathématiques Pures et Appliquées, 2014Co-Authors: Habib Ammari, Pol Grasland-mongrain, Pierre Millien, Laurent Seppecher, Jin Keun SeoAbstract:We provide a mathematical analysis and a numerical framework for Lorentz force electrical Conductivity imaging. Ultrasonic vibration of a tissue in the presence of a static magnetic field induces an electrical current by the Lorentz force. This current can be detected by electrodes placed around the tissue; it is proportional to the velocity of the ultrasonic pulse, but depends nonlinearly on the Conductivity Distribution. The imaging problem is to reconstruct the Conductivity Distribution from measurements of the induced current. To solve this nonlinear inverse problem, we first make use of a virtual potential to relate explicitly the current measurements to the Conductivity Distribution and the velocity of the ultrasonic pulse. Then, by applying a Wiener filter to the measured data, we reduce the problem to imaging the Conductivity from an internal electric current density. We first introduce an optimal control method for solving such a problem. A new direct reconstruction scheme involving a partial differential equation is then proposed based on viscosity-type regularization to a transport equation satisfied by the current density field. We prove that solving such an equation yields the true Conductivity Distribution as the regularization parameter approaches zero. We also test both schemes numerically in the presence of measurement noise, quantify their stability and resolution, and compare their performance.
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a posteriori error estimate and convergence analysis for Conductivity image reconstruction in mreit
Siam Journal on Applied Mathematics, 2010Co-Authors: Jijun Liu, Jin Keun Seo, Eung Je WooAbstract:Magnetic resonance electrical impedance tomography (MREIT) takes advantage of internal information to solve its nonlinear inverse problem of recovering a Conductivity Distribution inside an imaging object. When we inject current into the imaging object, there occurs a Distribution of internal magnetic flux density $\mathbf{B}=(B_x,B_y,B_z)$. In MREIT we utilize a magnetic resonance imaging scanner with its main magnetic field in the z direction to acquire $B_z$ data. The harmonic $B_z$ algorithm was invented in 2001 to reconstruct cross-sectional Conductivity images from $B_z$ data sets subject to multiple injection currents. Utilizing internal $B_z$ data, it overcomes the inherent ill posedness in electrical impedance tomography. We can set up the inverse problem in MREIT as a coefficient identification problem of finding $\sigma$ appearing in $\nabla\cdot(\sigma\nabla u)=0$ from acquired data of the z component of $\nabla\times(\sigma\nabla u)$. The harmonic $B_z$ algorithm has shown an excellent perfor...
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factorization method and its physical justification in frequency difference electrical impedance tomography
IEEE Transactions on Medical Imaging, 2010Co-Authors: Bastian Harrach, Jin Keun Seo, Eung Je WooAbstract:Time-difference electrical impedance tomography (tdEIT) requires two data sets measured at two different times. The difference between them is utilized to produce images of time-dependent changes in a complex Conductivity Distribution inside the human body. Frequency-difference EIT (fdEIT) was proposed to image frequency-dependent changes of a complex Conductivity Distribution. It has potential applications in tumor and stroke imaging since it can visualize an anomaly without requiring any time-reference data obtained in the absence of an anomaly. In this paper, we provide a rigorous analysis for the detectability of an anomaly based on a constructive and quantitative physical correlation between a measured fdEIT data set and an anomaly. From this, we propose a new noniterative frequency-difference anomaly detection method called the factorization method (FM) and elaborate its physical justification. To demonstrate its practical applicability, we performed fdEIT phantom imaging experiments using a multifrequency EIT system. Applying the FM to measured frequency-difference boundary voltage data sets, we could quantitatively evaluate indicator functions inside the imaging domain, of which values at each position reveal presence or absence of an anomaly. We found that the FM successfully localizes anomalies inside an imaging domain with a frequency-dependent complex Conductivity Distribution. We propose the new FM as an anomaly detection algorithm in fdEIT for potential applications in tumor and stroke imaging.
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magnetic resonance electrical impedance tomography mreit for high resolution Conductivity imaging
Physiological Measurement, 2008Co-Authors: Eung Je Woo, Jin Keun SeoAbstract:Cross-sectional imaging of an electrical Conductivity Distribution inside the human body has been an active research goal in impedance imaging. By injecting current into an electrically conducting object through surface electrodes, we induce current density and voltage Distributions. Based on the fact that these are determined by the Conductivity Distribution as well as the geometry of the object and the adopted electrode configuration, electrical impedance tomography (EIT) reconstructs cross-sectional Conductivity images using measured current-voltage data on the surface. Unfortunately, there exist inherent technical difficulties in EIT. First, the relationship between the boundary current-voltage data and the internal Conductivity Distribution bears a nonlinearity and low sensitivity, and hence the inverse problem of recovering the Conductivity Distribution is ill posed. Second, it is difficult to obtain accurate information on the boundary geometry and electrode positions in practice, and the inverse problem is sensitive to these modeling errors as well as measurement artifacts and noise. These result in EIT images with a poor spatial resolution. In order to produce high-resolution Conductivity images, magnetic resonance electrical impedance tomography (MREIT) has been lately developed. Noting that injection current produces a magnetic as well as electric field inside the imaging object, we can measure the induced internal magnetic flux density data using an MRI scanner. Utilization of the internal magnetic flux density is the key idea of MREIT to overcome the technical difficulties in EIT. Following original ideas on MREIT in early 1990s, there has been a rapid progress in its theory, algorithm and experimental techniques. The technique has now advanced to the stage of human experiments. Though it is still a few steps away from routine clinical use, its potential is high as a new impedance imaging modality providing Conductivity images with a spatial resolution of a few millimeters or less. This paper reviews MREIT from the basics to the most recent research outcomes. Focusing on measurement techniques and experimental methods rather than mathematical issues, we summarize what has been done and what needs to be done. Suggestions for future research directions, possible applications in biomedicine, biology, chemistry and material science are discussed.