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Sajib SZK, Sadleir R. Magnetic Resonance Electrical Impedance Tomography. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1380:157-183. [PMID: 36306098 DOI: 10.1007/978-3-031-03873-0_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Magnetic Resonance Electrical Impedance Tomography (MREIT) is a high-resolution bioimpedance imaging technique that has developed over a period beginning in the early 1990s to measure low-frequency (<1 kHz) tissue electrical properties. Low-frequency electrical properties are particularly important because they provide valuable information on cell structures and ionic composition of tissues, which may be very useful for diagnostic purposes. MREIT uses one component of the magnetic flux density data induced due to an exogenous-current administration, measured using an MRI machine, to reconstruct isotropic or anisotropic electrical property distributions. The MREIT technique typically requires two linearly independent current administrations to reconstruct conductivity uniquely. Since its invention, researchers have explored its potential for measuring electrical conductivity in regions such as the brain and muscle tissue. It has also been investigated in disease models, for example, cerebral ischemia and early tumor detection. In this chapter, we aim to provide a solid foundation of the different MREIT image reconstruction algorithms, including both isotropic and anisotropic conductivity reconstruction approaches. We will also explore the newly developed diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) method, a practical method for anisotropic tissue property imaging, at the end of the chapter.
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Affiliation(s)
- Saurav Z K Sajib
- School of Biological Health System Engineering, Arizona State University, Tempe, AZ, USA
| | - Rosalind Sadleir
- School of Biological Health System Engineering, Arizona State University, Tempe, AZ, USA.
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Minhas AS, Chauhan M, Fu F, Sadleir R. Evaluation of magnetohydrodynamic effects in magnetic resonance electrical impedance tomography at ultra-high magnetic fields. Magn Reson Med 2018; 81:2264-2276. [PMID: 30450638 DOI: 10.1002/mrm.27534] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 08/25/2018] [Accepted: 08/27/2018] [Indexed: 11/09/2022]
Abstract
PURPOSE Artifacts observed in experimental magnetic resonance electrical impedance tomography images were hypothesized to be because of magnetohydrodynamic (MHD) effects. THEORY AND METHODS Simulations of MREIT acquisition in the presence of MHD and electrical current flow were performed to confirm findings. Laminar flow and (electrostatic) electrical conduction equations were bidirectionally coupled via Lorentz force equations, and finite element simulations were performed to predict flow velocity as a function of time. Gradient sequences used in spin-echo and gradient echo acquisitions were used to calculate overall effects on MR phase images for different electrical current application or phase-encoding directions. RESULTS Calculated and experimental phase images agreed relatively well, both qualitatively and quantitatively, with some exceptions. Refocusing pulses in spin echo sequences did not appear to affect experimental phase images. CONCLUSION MHD effects were confirmed as the cause of observed experimental phase changes in MREIT images obtained at high fields. These findings may have implications for quantitative measurement of viscosity using MRI techniques. Methods developed here may be also important in studies of safety and in vivo artifacts observed in high field MRI systems.
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Affiliation(s)
- Atul S Minhas
- Faculty of Science and Engineering, School of Engineering, Macquarie University, Sydney, NSW, Australia
| | - Munish Chauhan
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona
| | - Fanrui Fu
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona
| | - Rosalind Sadleir
- School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona
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Sadleir RJ, Fu F, Chauhan M. Functional magnetic resonance electrical impedance tomography (fMREIT) sensitivity analysis using an active bidomain finite-element model of neural tissue. Magn Reson Med 2018; 81:602-614. [PMID: 29770490 DOI: 10.1002/mrm.27351] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 04/06/2018] [Accepted: 04/17/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE A direct method of imaging neural activity was simulated to determine typical signal sizes. METHODS An active bidomain finite-element model was used to estimate approximate perturbations in MR phase data as a result of neural tissue activity, and when an external MR electrical impedance tomography imaging current was added to the region containing neural current sources. RESULTS Modeling-predicted, activity-related conductivity changes should produce measurable differential phase signals in practical MR electrical impedance tomography experiments conducted at moderate resolution at noise levels typical of high field systems. The primary dependence of MR electrical impedance tomography phase contrast on membrane conductivity changes, and not source strength, was demonstrated. CONCLUSION Because the injected imaging current may also affect the level of activity in the tissue of interest, this technique can be used synergistically with neuromodulation techniques such as deep brain stimulation, to examine mechanisms of action.
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Affiliation(s)
- Rosalind J Sadleir
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona
| | - Fanrui Fu
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona
| | - Munish Chauhan
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona
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Chauhan M, Indahlastari A, Kasinadhuni AK, Schar M, Mareci TH, Sadleir RJ. Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:966-976. [PMID: 29610075 PMCID: PMC5963516 DOI: 10.1109/tmi.2017.2783348] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We present the first in vivo images of anisotropic conductivity distribution in the human head, measured at a frequency of approximately 10 Hz. We used magnetic resonance electrical impedance tomography techniques to encode phase changes caused by current flow within the head via two independent electrode pairs. These results were then combined with diffusion tensor imaging data to reconstruct full anisotropic conductivity distributions in 5-mm-thick slices of the brains of two participants. Conductivity values recovered in this paper were broadly consistent with literature values. We anticipate that this technique will be of use in many areas of neuroscience, most importantly in functional imaging via inverse electroencephalogram. Future studies will involve pulse sequence acceleration to maximize brain coverage and resolution.
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Sajib SZK, Katoch N, Kim HJ, Kwon OI, Woo EJ. Software Toolbox for Low-Frequency Conductivity and Current Density Imaging Using MRI. IEEE Trans Biomed Eng 2018; 64:2505-2514. [PMID: 28767360 DOI: 10.1109/tbme.2017.2732502] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI. METHODS To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment. RESULTS The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox. CONCLUSION Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes. OBJECTIVE Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI. METHODS To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment. RESULTS The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox. CONCLUSION Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes.
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Affiliation(s)
| | - Nitish Katoch
- Department of Biomedical EngineeringKyung Hee University
| | | | - Oh In Kwon
- Department of MathematicsKonkuk University
| | - Eung Je Woo
- Department of Biomedical Engineering, Kyung Hee University, Seoul, South Korea
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Sadleir RJ, Fu F, Falgas C, Holland S, Boggess M, Grant SC, Woo EJ. Direct detection of neural activity in vitro using magnetic resonance electrical impedance tomography (MREIT). Neuroimage 2017; 161:104-119. [PMID: 28818695 PMCID: PMC5696120 DOI: 10.1016/j.neuroimage.2017.08.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 07/19/2017] [Accepted: 08/01/2017] [Indexed: 11/18/2022] Open
Abstract
We describe a sequence of experiments performed in vitro to verify the existence of a new magnetic resonance imaging contrast - Magnetic Resonance Electrical Impedance Tomography (MREIT) -sensitive to changes in active membrane conductivity. We compared standard deviations in MREIT phase data from spontaneously active Aplysia abdominal ganglia in an artificial seawater background solution (ASW) with those found after treatment with an excitotoxic solution (KCl). We found significant increases in MREIT treatment cases, compared to control ganglia subject to extra ASW. This distinction was not found in phase images from the same ganglia using no imaging current. Further, significance and effect size depended on the amplitude of MREIT imaging current used. We conclude that our observations were linked to changes in cell conductivity caused by activity. Functional MREIT may have promise as a more direct method of functional neuroimaging than existing methods that image correlates of blood flow such as BOLD fMRI.
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Affiliation(s)
- Rosalind J Sadleir
- School of Biological and Health Systems Engineering, Arizona State University, 500 E. Tyler Mall, Tempe, AZ 85287-9709, USA.
| | - Fanrui Fu
- School of Biological and Health Systems Engineering, Arizona State University, 500 E. Tyler Mall, Tempe, AZ 85287-9709, USA
| | - Corey Falgas
- Department of Chemical and Biomedical Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA
| | - Stephen Holland
- Department of Chemical and Biomedical Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA
| | - May Boggess
- School of Mathematical and Statistical Sciences, Arizona State University, 901 S. Palm Walk, Tempe, AZ 85287-1804, USA
| | - Samuel C Grant
- Department of Chemical and Biomedical Engineering, Florida A&M University-Florida State University College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA
| | - Eung Je Woo
- Dept. of Biomedical Engineering, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea
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Kwon OI, Sajib SZK, Sersa I, Oh TI, Jeong WC, Kim HJ, Woo EJ. Current Density Imaging During Transcranial Direct Current Stimulation Using DT-MRI and MREIT: Algorithm Development and Numerical Simulations. IEEE Trans Biomed Eng 2015; 63:168-75. [PMID: 26111387 DOI: 10.1109/tbme.2015.2448555] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Transcranial direct current stimulation (tDCS) is a neuromodulatory technique for neuropsychiatric diseases and neurological disorders. In the tDCS treatment, dc current is injected into the head through a pair of electrodes attached on the scalp over a target region. A current density imaging method is needed to quantitatively visualize the internal current density distribution during the tDCS treatment. METHODS We developed a novel current density image reconstruction algorithm using 1) a subject specific segmented 3-D head model, 2) diffusion tensor data, and 3) magnetic flux density data induced by the tDCS current. We acquired T1 weighted and diffusion tensor images of the head using the MRI scanner before the treatment. During the treatment, we can measure the induced magnetic flux density data using a magnetic resonance electrical impedance tomography (MREIT) pulse sequence. In this paper, the magnetic flux density data were numerically generated. RESULTS Numerical simulation results show that the proposed method successfully recovers the current density distribution including the effects of the anisotropic, as well as isotropic conductivity values of different tissues in the head. CONCLUSION The proposed current density imaging method using DT-MRI and MREIT can reliably recover cross-sectional images of the current density distribution during the tDCS treatment. SIGNIFICANCE Success of the tDCS treatment depends on a precise determination of the induced current density distribution within different anatomical structures of the brain. Quantitative visualization of the current density distribution in the brain will play an important role in understanding the effects of the electrical stimulation.
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Lee SH, Park H. Parametric response mapping of longitudinal PET scans and their use in detecting changes in Alzheimer’s diseases. Biomed Eng Lett 2014. [DOI: 10.1007/s13534-014-0120-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
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Sadleir RJ, Sajib SZK, Kim HJ, Kwon OI, Woo EJ. Simulations and phantom evaluations of magnetic resonance electrical impedance tomography (MREIT) for breast cancer detection. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2013; 230:40-49. [PMID: 23435264 DOI: 10.1016/j.jmr.2013.01.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2012] [Revised: 01/22/2013] [Accepted: 01/25/2013] [Indexed: 06/01/2023]
Abstract
MREIT is a new imaging modality that can be used to reconstruct high-resolution conductivity images of the human body. Since conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues, breast imaging using MREIT may provide a new noninvasive way of detecting early stage of cancer. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart and evaluated the ability of MREIT to detect cancerous anomalies in a background material with similar electrical properties to breast tissue. We performed numerical simulations of various scenarios in breast MREIT including assessment of the effects of fat inclusions and effects related to noise levels, such as changing the amplitude of injected currents, effect of added noise and number of averages. Phantom results showed straightforward detection of cancerous anomalies in a background was possible with low currents and few averages. The simulation results showed it should be possible to detect a cancerous anomaly in the breast, while restricting the maximal current density in the heart below published levels for nerve excitation.
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Affiliation(s)
- Rosalind J Sadleir
- Department of Biomedical Engineering, Kyung Hee University, Yongin, Gyeonggi, Republic of Korea
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Meng ZJ, Sajib SZK, Chauhan M, Sadleir RJ, Kim HJ, Kwon OI, Woo EJ. Numerical simulations of MREIT conductivity imaging for brain tumor detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:704829. [PMID: 23737862 PMCID: PMC3657440 DOI: 10.1155/2013/704829] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Revised: 02/21/2013] [Accepted: 04/05/2013] [Indexed: 01/21/2023]
Abstract
Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged.
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Affiliation(s)
- Zi Jun Meng
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
| | - Saurav Z. K. Sajib
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
| | - Munish Chauhan
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
| | - Rosalind J. Sadleir
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Hyung Joong Kim
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
| | - Oh In Kwon
- Department of Mathematics, Konkuk University, Seoul, Republic of Korea
| | - Eung Je Woo
- Department of Biomedical Engineering, Impedance Imaging Research Center (IIRC), Kyung Hee University, Yongin, Republic of Korea
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Sajib SZK, Kim HJ, Kim YT, Jeong WC, Oh TI, Woo EJ. Potential of MREIT conductivity imaging to detect breast cancer: experimental and numerical simulation studies. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:440-3. [PMID: 23365923 DOI: 10.1109/embc.2012.6345962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues. Breast imaging using MREIT (Magnetic Resonance Electrical Impedance Tomography) may provide a new noninvasive way of detecting breast cancer in its early stage. In breast MREIT, the conductivity image quality highly depends on the amount of injected currents assuming a certain signal-to-noise ratio (SNR) of an MRI scanner. The injected current should not produce any significant adverse effect especially on the nerve conduction system of the heart and still distinguish a small cancerous anomaly inside the breast. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. From breast phantom experiments, we evaluated practical amounts of noise in measured magnetic flux density data. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart. We performed numerical simulations of various scenarios in breast MREIT including different amplitudes of injected currents and predicted SNRs of MR images related with imaging parameters. Simulation results are promising to show that we may detect a cancerous anomaly in the breast while restricting the maximal current density inside the heart below a level of nerve excitation.
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Affiliation(s)
- Saurav Z K Sajib
- Department of Biomedical Engineering, Kyung Hee University, Gyeonggi-do 446-701 KOREA
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Meng Z, Sajib SZK, Chauhan M, Jeong WC, Kim YT, Kim HJ, Woo EJ. Improved conductivity image of human lower extremity using MREIT with chemical shift artifact correction. Biomed Eng Lett 2012. [DOI: 10.1007/s13534-012-0052-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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