The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Eung Je Woo - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of electrical Conductivity and anisotropy in muscle tissues using Conductivity Tensor imaging (CTI)
AIP Advances, 2020Co-Authors: Bup Kyung Choi, Hyung Joong Kim, Nitish Katoch, Ji Ae Park, Jin Woong Kim, Eung Je WooAbstract:Low-frequency Conductivity of an anisotropic tissue is associated with its cellular structure. Imaging of the Conductivity Tensor inside the human body could prove invaluable to analyses of interactions between electromagnetic fields and biological systems, such as predictions of current pathways during electrical stimulation. Since the muscle is the most abundant anisotropic tissue in the human body, in vitro and in vivo muscle Conductivity Tensor assessment has been attempted. In this study, we conducted phantom imaging of biological tissues using Conductivity Tensor imaging (CTI) to validate its in vivo usefulness. We constructed phantoms using bovine and/or porcine muscles and performed a CTI experiment using MRI. High-frequency Conductivity was first obtained using B1 mapping with a multi-echo spin-echo pulse sequence. Information about the cellular space was obtained with a multi-b diffusion Tensor imaging sequence. We combined the data from these separate scans to reconstruct the Conductivity Tensor images of the phantoms. The low-frequency and high-frequency conductivities of the muscle tissues in the phantoms were compared with the corresponding values measured by an impedance analyzer. The anisotropy of each muscle tissue was quantified as an anisotropy ratio (AR), defined as the ratio of the eigenvalues of a Conductivity Tensor along the longitudinal direction to those along the transversal directions. The isotropic Conductivity and Conductivity Tensor in bovine muscles were less than those of porcine muscles. However, the anisotropy was stronger in bovine muscles based on the AR values by fiber directions. Current CTI is a promising noninvasive tool for evaluation of the muscle microstructure.
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Validation of Conductivity Tensor imaging using giant vesicle suspensions with different ion mobilities.
Biomedical engineering online, 2020Co-Authors: Bup Kyung Choi, Oh In Kwon, Hyung Joong Kim, Nitish Katoch, Ji Ae Park, Eung Je WooAbstract:Electrical Conductivity of a biological tissue at low frequencies can be approximately expressed as a Tensor. Noting that cross-sectional imaging of a low-frequency Conductivity Tensor distribution inside the human body has wide clinical applications of many bioelectromagnetic phenomena, a new Conductivity Tensor imaging (CTI) technique has been lately developed using an MRI scanner. Since the technique is based on a few assumptions between mobility and diffusivity of ions and water molecules, experimental validations are needed before applying it to clinical studies. We designed two Conductivity phantoms each with three compartments. The compartments were filled with electrolytes and/or giant vesicle suspensions. The giant vesicles were cell-like materials with thin insulating membranes. We controlled viscosity of the electrolytes and the giant vesicle suspensions to change ion mobility and therefore Conductivity values. The Conductivity values of the electrolytes and giant vesicle suspensions were measured using an impedance analyzer before CTI experiments. A 9.4-T research MRI scanner was used to reconstruct Conductivity Tensor images of the phantoms. The CTI technique successfully reconstructed Conductivity Tensor images of the phantoms with a voxel size of $$0.5\times 0.5\times 0.5\hbox { mm}^3$$. The relative $$L^2$$ errors between the Conductivity values measured by the impedance analyzer and those reconstructed by the MRI scanner was between 1.1 and 11.5. The accuracy of the new CTI technique was estimated to be high enough for most clinical applications. Future studies of animal models and human subjects should be pursued to show the clinical efficacy of the CTI technique.
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Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension
IEEE transactions on medical imaging, 2018Co-Authors: Nitish Katoch, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Bup Kyung Choi, Eunah Lee, Eung Je WooAbstract:Human brain mapping of low-frequency electrical Conductivity Tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless Conductivity Tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed Conductivity Tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter Conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96–3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12–1.19 with a mean value of 1.16. The three diagonal components of the reconstructed Conductivity Tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed Conductivity Tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency Conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the Conductivity Tensor.
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Electrodeless Conductivity Tensor imaging (CTI) using MRI: basic theory and animal experiments
Biomedical Engineering Letters, 2018Co-Authors: Saurav Z.k. Sajib, Oh In Kwon, Hyung Joong Kim, Eung Je WooAbstract:The electrical Conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic Conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a Tensor and properly quantified, the Conductivity Tensor can provide diagnostic information of numerous diseases. Imaging Conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct Conductivity Tensor images using an MRI scanner without current injection. This electrodeless method of Conductivity Tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic Conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo Conductivity Tensor images of canine brains. Depending on the details of the implementation, it may produce Conductivity contrast images for Conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation.
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Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT
IEEE transactions on medical imaging, 2017Co-Authors: Woo Chul Jeong, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Nitish Katoch, Eung Je WooAbstract:We present in vivo images of anisotropic electrical Conductivity Tensor distributions inside canine brains using diffusion Tensor magnetic resonance electrical impedance tomography (DT-MREIT). The Conductivity Tensor is represented as a product of an ion mobility Tensor and a scale factor of ion concentrations. Incorporating directional mobility information from water diffusion Tensors, we developed a stable process to reconstruct anisotropic Conductivity Tensor images from measured magnetic flux density data using an MRI scanner. Devising a new image reconstruction algorithm, we reconstructed anisotropic Conductivity Tensor images of two canine brains with a pixel size of 1.25 mm. Though the reconstructed Conductivity values matched well in general with those measured by using invasive probing methods, there were some discrepancies as well. The degree of white matter anisotropy was 2 to 4.5, which is smaller than previous findings of 5 to 10. The reconstructed Conductivity value of the cerebrospinal fluid was about 1.3 S/m, which is smaller than previous measurements of about 1.8 S/m. Future studies of in vivo imaging experiments with disease models should follow this initial trial to validate clinical significance of DT-MREIT as a new diagnostic imaging modality. Applications in modeling and simulation studies of bioelectromagnetic phenomena including source imaging and electrical stimulation are also promising.
B. Murat Eyuboglu - One of the best experts on this subject based on the ideXlab platform.
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Low-frequency Conductivity Tensor imaging with a single current injection using DT-MREIT.
Physics in medicine and biology, 2021Co-Authors: Mehdi Sadighi, Mert Şişman, Berk C Açıkgöz, Hasan H Eroğlu, B. Murat EyubogluAbstract:Diffusion Tensor - magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic Conductivity distribution employing diffusion Tensor imaging (DTI) and magnetic resonance electrical impedance tomography (MREIT) techniques. DT-MREIT is based on the linear relationship between the Conductivity and water self-diffusion Tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic Conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the Conductivity Tensor images are reconstructed using the diffusion Tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the Conductivity Tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic Conductivity distribution using the proposed single current DT-MREIT.
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EMBC - J-based Magnetic Resonance Conductivity Tensor Imaging (MRCTI) at 3 T.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2014Co-Authors: Mehdi Sadighi, C. Goksu, B. Murat EyubogluAbstract:In this study, current density (J) - based Magnetic Resonance Conductivity Tensor Imaging (MRCTI) reconstruction algorithms namely, the Anisotropic Equipotential Projection (AEPP), the Anisotropic J-Substitution (AJS) and the Anisotropic Hybrid J-Substitution (AHJS) algorithms are implemented to reconstruct Conductivity Tensor images of a physical phantom using a 3T magnetic resonance imaging system. 10mA current pulses are injected in synchrony with a conventional spin-echo pulse sequence. Furthermore, a new J-based hybrid algorithm namely, the Anisotropic Hybrid Equipotential Projection (AHEPP) is proposed. In addition, reconstruction performances of the four algorithms are evaluated.
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Practical Realization of Magnetic Resonance Conductivity Tensor Imaging (MRCTI)
IEEE transactions on medical imaging, 2012Co-Authors: Evren Degirmenci, B. Murat EyubogluAbstract:Magnetic resonance Conductivity Tensor imaging (MRCTI) is an emerging modality which reconstructs images of anisotropic Conductivity distribution within a volume conductor. Images are reconstructed based on magnetic flux density distribution induced by an externally applied probing current, together with a resultant surface potential value. The induced magnetic flux density distribution is measured using magnetic resonance current density imaging techniques. In this study, MRCTI data acquisition is experimentally implemented and anisotropic Conductivity images of test phantoms are reconstructed using recently proposed MRCTI reconstruction algorithms.
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Image Reconstruction in Magnetic Resonance Conductivity Tensor Imaging (MRCTI)
IEEE transactions on medical imaging, 2011Co-Authors: Evren Degirmenci, B. Murat EyubogluAbstract:Almost all magnetic resonance electrical impedance tomography (MREIT) reconstruction algorithms proposed to date assume isotropic Conductivity in order to simplify the image reconstruction. However, it is well known that most of biological tissues have anisotropic Conductivity values. In this study, four novel anisotropic Conductivity reconstruction algorithms are proposed to reconstruct high resolution Conductivity Tensor images. Performances of these four algorithms and a previously proposed algorithm are evaluated in several aspects and compared.
Oh In Kwon - One of the best experts on this subject based on the ideXlab platform.
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Validation of Conductivity Tensor imaging using giant vesicle suspensions with different ion mobilities.
Biomedical engineering online, 2020Co-Authors: Bup Kyung Choi, Oh In Kwon, Hyung Joong Kim, Nitish Katoch, Ji Ae Park, Eung Je WooAbstract:Electrical Conductivity of a biological tissue at low frequencies can be approximately expressed as a Tensor. Noting that cross-sectional imaging of a low-frequency Conductivity Tensor distribution inside the human body has wide clinical applications of many bioelectromagnetic phenomena, a new Conductivity Tensor imaging (CTI) technique has been lately developed using an MRI scanner. Since the technique is based on a few assumptions between mobility and diffusivity of ions and water molecules, experimental validations are needed before applying it to clinical studies. We designed two Conductivity phantoms each with three compartments. The compartments were filled with electrolytes and/or giant vesicle suspensions. The giant vesicles were cell-like materials with thin insulating membranes. We controlled viscosity of the electrolytes and the giant vesicle suspensions to change ion mobility and therefore Conductivity values. The Conductivity values of the electrolytes and giant vesicle suspensions were measured using an impedance analyzer before CTI experiments. A 9.4-T research MRI scanner was used to reconstruct Conductivity Tensor images of the phantoms. The CTI technique successfully reconstructed Conductivity Tensor images of the phantoms with a voxel size of $$0.5\times 0.5\times 0.5\hbox { mm}^3$$. The relative $$L^2$$ errors between the Conductivity values measured by the impedance analyzer and those reconstructed by the MRI scanner was between 1.1 and 11.5. The accuracy of the new CTI technique was estimated to be high enough for most clinical applications. Future studies of animal models and human subjects should be pursued to show the clinical efficacy of the CTI technique.
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Anisotropic Conductivity Tensor by analyzing diffusion Tensor for electrical brain stimulation (EBS).
Physics in medicine and biology, 2018Co-Authors: Mun Bae Lee, Hyung Joong Kim, Yeon Hyang Kim, Oh In KwonAbstract:Electrical brain stimulation (EBS) is a promising medical treatment method for brain neurological disorders through the direct or indirect excitation by injecting an electric current. At present, it is difficult to directly measure the distribution of the electric current delivered by electrodes inside tissues. By applying low-frequency ([Formula: see text]1 kHz) external electrical brain stimulation (EBS), the low-frequency Conductivity around the cells is uneven due to asymmetric cellular structures. We propose a method of electrical property imaging using the measured one component magnetic flux density by EBS and diffusion Tensor imaging (DTI). The low-frequency electrical anisotropic Conductivity Tensor can be decomposed into the ion concentration and the mobility Tensor of charge carriers. By analyzing the role of the diffusion Tensor, we reconstruct the apparent anisotropic Tensor by EBS using the [Formula: see text]-component of measured magnetic flux density data and the estimated diffusion Tensor. Using only the measured [Formula: see text]-component of magnetic flux density, the orthotropic Conductivity Tensor can be approximately recovered. The orthotropic Conductivity Tensor is not exact, but only reflects the extracellular space (ECS) effects. By comparing the components of orthotropic Tensor and diffusion Tensor, we stably determine a scale factor which primarily reflects the concentration of total ions in the extracellular space (ECS). Animal experiments verify that the proposed method recovers the anisotropic Conductivity Tensor which can visualize electrical properties during EBS of the brain. A direct reconstruction method for the apparent anisotropic Conductivity Tensor imaging during EBS was proposed to analyze unknown effects to brain tissue.
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Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension
IEEE transactions on medical imaging, 2018Co-Authors: Nitish Katoch, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Bup Kyung Choi, Eunah Lee, Eung Je WooAbstract:Human brain mapping of low-frequency electrical Conductivity Tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless Conductivity Tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed Conductivity Tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter Conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96–3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12–1.19 with a mean value of 1.16. The three diagonal components of the reconstructed Conductivity Tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed Conductivity Tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency Conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the Conductivity Tensor.
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Electrodeless Conductivity Tensor imaging (CTI) using MRI: basic theory and animal experiments
Biomedical Engineering Letters, 2018Co-Authors: Saurav Z.k. Sajib, Oh In Kwon, Hyung Joong Kim, Eung Je WooAbstract:The electrical Conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic Conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a Tensor and properly quantified, the Conductivity Tensor can provide diagnostic information of numerous diseases. Imaging Conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct Conductivity Tensor images using an MRI scanner without current injection. This electrodeless method of Conductivity Tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic Conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo Conductivity Tensor images of canine brains. Depending on the details of the implementation, it may produce Conductivity contrast images for Conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation.
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Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT
IEEE transactions on medical imaging, 2017Co-Authors: Woo Chul Jeong, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Nitish Katoch, Eung Je WooAbstract:We present in vivo images of anisotropic electrical Conductivity Tensor distributions inside canine brains using diffusion Tensor magnetic resonance electrical impedance tomography (DT-MREIT). The Conductivity Tensor is represented as a product of an ion mobility Tensor and a scale factor of ion concentrations. Incorporating directional mobility information from water diffusion Tensors, we developed a stable process to reconstruct anisotropic Conductivity Tensor images from measured magnetic flux density data using an MRI scanner. Devising a new image reconstruction algorithm, we reconstructed anisotropic Conductivity Tensor images of two canine brains with a pixel size of 1.25 mm. Though the reconstructed Conductivity values matched well in general with those measured by using invasive probing methods, there were some discrepancies as well. The degree of white matter anisotropy was 2 to 4.5, which is smaller than previous findings of 5 to 10. The reconstructed Conductivity value of the cerebrospinal fluid was about 1.3 S/m, which is smaller than previous measurements of about 1.8 S/m. Future studies of in vivo imaging experiments with disease models should follow this initial trial to validate clinical significance of DT-MREIT as a new diagnostic imaging modality. Applications in modeling and simulation studies of bioelectromagnetic phenomena including source imaging and electrical stimulation are also promising.
Hyung Joong Kim - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of electrical Conductivity and anisotropy in muscle tissues using Conductivity Tensor imaging (CTI)
AIP Advances, 2020Co-Authors: Bup Kyung Choi, Hyung Joong Kim, Nitish Katoch, Ji Ae Park, Jin Woong Kim, Eung Je WooAbstract:Low-frequency Conductivity of an anisotropic tissue is associated with its cellular structure. Imaging of the Conductivity Tensor inside the human body could prove invaluable to analyses of interactions between electromagnetic fields and biological systems, such as predictions of current pathways during electrical stimulation. Since the muscle is the most abundant anisotropic tissue in the human body, in vitro and in vivo muscle Conductivity Tensor assessment has been attempted. In this study, we conducted phantom imaging of biological tissues using Conductivity Tensor imaging (CTI) to validate its in vivo usefulness. We constructed phantoms using bovine and/or porcine muscles and performed a CTI experiment using MRI. High-frequency Conductivity was first obtained using B1 mapping with a multi-echo spin-echo pulse sequence. Information about the cellular space was obtained with a multi-b diffusion Tensor imaging sequence. We combined the data from these separate scans to reconstruct the Conductivity Tensor images of the phantoms. The low-frequency and high-frequency conductivities of the muscle tissues in the phantoms were compared with the corresponding values measured by an impedance analyzer. The anisotropy of each muscle tissue was quantified as an anisotropy ratio (AR), defined as the ratio of the eigenvalues of a Conductivity Tensor along the longitudinal direction to those along the transversal directions. The isotropic Conductivity and Conductivity Tensor in bovine muscles were less than those of porcine muscles. However, the anisotropy was stronger in bovine muscles based on the AR values by fiber directions. Current CTI is a promising noninvasive tool for evaluation of the muscle microstructure.
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Validation of Conductivity Tensor imaging using giant vesicle suspensions with different ion mobilities.
Biomedical engineering online, 2020Co-Authors: Bup Kyung Choi, Oh In Kwon, Hyung Joong Kim, Nitish Katoch, Ji Ae Park, Eung Je WooAbstract:Electrical Conductivity of a biological tissue at low frequencies can be approximately expressed as a Tensor. Noting that cross-sectional imaging of a low-frequency Conductivity Tensor distribution inside the human body has wide clinical applications of many bioelectromagnetic phenomena, a new Conductivity Tensor imaging (CTI) technique has been lately developed using an MRI scanner. Since the technique is based on a few assumptions between mobility and diffusivity of ions and water molecules, experimental validations are needed before applying it to clinical studies. We designed two Conductivity phantoms each with three compartments. The compartments were filled with electrolytes and/or giant vesicle suspensions. The giant vesicles were cell-like materials with thin insulating membranes. We controlled viscosity of the electrolytes and the giant vesicle suspensions to change ion mobility and therefore Conductivity values. The Conductivity values of the electrolytes and giant vesicle suspensions were measured using an impedance analyzer before CTI experiments. A 9.4-T research MRI scanner was used to reconstruct Conductivity Tensor images of the phantoms. The CTI technique successfully reconstructed Conductivity Tensor images of the phantoms with a voxel size of $$0.5\times 0.5\times 0.5\hbox { mm}^3$$. The relative $$L^2$$ errors between the Conductivity values measured by the impedance analyzer and those reconstructed by the MRI scanner was between 1.1 and 11.5. The accuracy of the new CTI technique was estimated to be high enough for most clinical applications. Future studies of animal models and human subjects should be pursued to show the clinical efficacy of the CTI technique.
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Anisotropic Conductivity Tensor by analyzing diffusion Tensor for electrical brain stimulation (EBS).
Physics in medicine and biology, 2018Co-Authors: Mun Bae Lee, Hyung Joong Kim, Yeon Hyang Kim, Oh In KwonAbstract:Electrical brain stimulation (EBS) is a promising medical treatment method for brain neurological disorders through the direct or indirect excitation by injecting an electric current. At present, it is difficult to directly measure the distribution of the electric current delivered by electrodes inside tissues. By applying low-frequency ([Formula: see text]1 kHz) external electrical brain stimulation (EBS), the low-frequency Conductivity around the cells is uneven due to asymmetric cellular structures. We propose a method of electrical property imaging using the measured one component magnetic flux density by EBS and diffusion Tensor imaging (DTI). The low-frequency electrical anisotropic Conductivity Tensor can be decomposed into the ion concentration and the mobility Tensor of charge carriers. By analyzing the role of the diffusion Tensor, we reconstruct the apparent anisotropic Tensor by EBS using the [Formula: see text]-component of measured magnetic flux density data and the estimated diffusion Tensor. Using only the measured [Formula: see text]-component of magnetic flux density, the orthotropic Conductivity Tensor can be approximately recovered. The orthotropic Conductivity Tensor is not exact, but only reflects the extracellular space (ECS) effects. By comparing the components of orthotropic Tensor and diffusion Tensor, we stably determine a scale factor which primarily reflects the concentration of total ions in the extracellular space (ECS). Animal experiments verify that the proposed method recovers the anisotropic Conductivity Tensor which can visualize electrical properties during EBS of the brain. A direct reconstruction method for the apparent anisotropic Conductivity Tensor imaging during EBS was proposed to analyze unknown effects to brain tissue.
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Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension
IEEE transactions on medical imaging, 2018Co-Authors: Nitish Katoch, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Bup Kyung Choi, Eunah Lee, Eung Je WooAbstract:Human brain mapping of low-frequency electrical Conductivity Tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless Conductivity Tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed Conductivity Tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter Conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96–3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12–1.19 with a mean value of 1.16. The three diagonal components of the reconstructed Conductivity Tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed Conductivity Tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency Conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the Conductivity Tensor.
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Electrodeless Conductivity Tensor imaging (CTI) using MRI: basic theory and animal experiments
Biomedical Engineering Letters, 2018Co-Authors: Saurav Z.k. Sajib, Oh In Kwon, Hyung Joong Kim, Eung Je WooAbstract:The electrical Conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic Conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a Tensor and properly quantified, the Conductivity Tensor can provide diagnostic information of numerous diseases. Imaging Conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct Conductivity Tensor images using an MRI scanner without current injection. This electrodeless method of Conductivity Tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic Conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo Conductivity Tensor images of canine brains. Depending on the details of the implementation, it may produce Conductivity contrast images for Conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation.
Saurav Z.k. Sajib - One of the best experts on this subject based on the ideXlab platform.
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Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension
IEEE transactions on medical imaging, 2018Co-Authors: Nitish Katoch, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Bup Kyung Choi, Eunah Lee, Eung Je WooAbstract:Human brain mapping of low-frequency electrical Conductivity Tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless Conductivity Tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed Conductivity Tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter Conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96–3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12–1.19 with a mean value of 1.16. The three diagonal components of the reconstructed Conductivity Tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed Conductivity Tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency Conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the Conductivity Tensor.
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Electrodeless Conductivity Tensor imaging (CTI) using MRI: basic theory and animal experiments
Biomedical Engineering Letters, 2018Co-Authors: Saurav Z.k. Sajib, Oh In Kwon, Hyung Joong Kim, Eung Je WooAbstract:The electrical Conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic Conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a Tensor and properly quantified, the Conductivity Tensor can provide diagnostic information of numerous diseases. Imaging Conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct Conductivity Tensor images using an MRI scanner without current injection. This electrodeless method of Conductivity Tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic Conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo Conductivity Tensor images of canine brains. Depending on the details of the implementation, it may produce Conductivity contrast images for Conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation.
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Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT
IEEE transactions on medical imaging, 2017Co-Authors: Woo Chul Jeong, Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Nitish Katoch, Eung Je WooAbstract:We present in vivo images of anisotropic electrical Conductivity Tensor distributions inside canine brains using diffusion Tensor magnetic resonance electrical impedance tomography (DT-MREIT). The Conductivity Tensor is represented as a product of an ion mobility Tensor and a scale factor of ion concentrations. Incorporating directional mobility information from water diffusion Tensors, we developed a stable process to reconstruct anisotropic Conductivity Tensor images from measured magnetic flux density data using an MRI scanner. Devising a new image reconstruction algorithm, we reconstructed anisotropic Conductivity Tensor images of two canine brains with a pixel size of 1.25 mm. Though the reconstructed Conductivity values matched well in general with those measured by using invasive probing methods, there were some discrepancies as well. The degree of white matter anisotropy was 2 to 4.5, which is smaller than previous findings of 5 to 10. The reconstructed Conductivity value of the cerebrospinal fluid was about 1.3 S/m, which is smaller than previous measurements of about 1.8 S/m. Future studies of in vivo imaging experiments with disease models should follow this initial trial to validate clinical significance of DT-MREIT as a new diagnostic imaging modality. Applications in modeling and simulation studies of bioelectromagnetic phenomena including source imaging and electrical stimulation are also promising.
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Reconstruction of apparent orthotropic Conductivity Tensor image using magnetic resonance electrical impedance tomography
Journal of Applied Physics, 2015Co-Authors: Saurav Z.k. Sajib, Oh In Kwon, Hyung Joong Kim, Woo Chul Jeong, Ji Eun Kim, Eung Je WooAbstract:Magnetic resonance electrical impedance tomography visualizes current density and/or Conductivity distributions inside an electrically conductive object. Injecting currents into the imaging object along at least two different directions, induced magnetic flux density data can be measured using a magnetic resonance imaging scanner. Without rotating the object inside the scanner, we can measure only one component of the magnetic flux density denoted as Bz. Since the biological tissues such as skeletal muscle and brain white matter show strong anisotropic properties, the reconstruction of anisotropic Conductivity Tensor is indispensable for the accurate observations in the biological systems. In this paper, we propose a direct method to reconstruct an axial apparent orthotropic Conductivity Tensor by using multiple Bz data subject to multiple injection currents. To investigate the anisotropic Conductivity properties, we first recover the internal current density from the measured Bz data. From the recovered ...
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Anisotropic Conductivity Tensor imaging in MREIT using directional diffusion rate of water molecules
Physics in Medicine and Biology, 2014Co-Authors: Oh In Kwon, Saurav Z.k. Sajib, Hyung Joong Kim, Woo Chul Jeong, Eung Je WooAbstract:Magnetic resonance electrical impedance tomography (MREIT) is an emerging method to visualize electrical Conductivity and/or current density images at low frequencies (below 1 KHz). Injecting currents into an imaging object, one component of the induced magnetic flux density is acquired using an MRI scanner for isotropic Conductivity image reconstructions. Diffusion Tensor MRI (DT-MRI) measures the intrinsic three-dimensional diffusion property of water molecules within a tissue. It characterizes the anisotropic water transport by the effective diffusion Tensor. Combining the DT-MRI and MREIT techniques, we propose a novel direct method for absolute Conductivity Tensor image reconstructions based on a linear relationship between the water diffusion Tensor and the electrical Conductivity Tensor. We first recover the projected current density, which is the best approximation of the internal current density one can obtain from the measured single component of the induced magnetic flux density. This enables us to estimate a scale factor between the diffusion Tensor and the Conductivity Tensor. Combining these values at all pixels with the acquired diffusion Tensor map, we can quantitatively recover the anisotropic Conductivity Tensor map. From numerical simulations and experimental verifications using a biological tissue phantom, we found that the new method overcomes the limitations of each method and successfully reconstructs both the direction and magnitude of the Conductivity Tensor for both the anisotropic and isotropic regions.