Her main research concerns Electrical impedance tomography, Iterative reconstruction, Algorithm, Mathematical analysis and Nuclear magnetic resonance. She interconnects Acoustics, Neumann boundary condition, Inverse problem and Voltage in the investigation of issues within Electrical impedance tomography. Her Iterative reconstruction research includes elements of Tomography, Current density imaging and Scanner.
The various areas that Jin Keun Seo examines in her Algorithm study include Sampling, Fourier analysis, Folding and Harmonic. Her work on Current, Distribution and Uniqueness as part of general Mathematical analysis study is frequently linked to Nabla symbol, therefore connecting diverse disciplines of science. Her research in Nuclear magnetic resonance intersects with topics in Magnetic flux and Computational physics.
Jin Keun Seo mainly investigates Electrical impedance tomography, Artificial intelligence, Inverse problem, Mathematical analysis and Algorithm. She combines subjects such as Acoustics, Sensitivity, Imaging phantom, Voltage and Biomedical engineering with her study of Electrical impedance tomography. The study incorporates disciplines such as Tomography, Computer vision and Pattern recognition in addition to Artificial intelligence.
Her work deals with themes such as Mathematical optimization and Nonlinear system, which intersect with Inverse problem. The concepts of her Algorithm study are interwoven with issues in Noise, Iterative reconstruction and Harmonic. Her study focuses on the intersection of Iterative reconstruction and fields such as Nuclear magnetic resonance with connections in the field of Image resolution, Electrical resistivity and conductivity, Computational physics and Scanner.
Jin Keun Seo spends much of her time researching Artificial intelligence, Deep learning, Electrical impedance tomography, Tomography and Pattern recognition. Her Artificial intelligence study combines topics from a wide range of disciplines, such as Cephalometric analysis, Machine learning, Ultrasound and Computer vision. Her work carried out in the field of Electrical impedance tomography brings together such families of science as Acoustics, Inverse problem, Learning based, Imaging phantom and Reconstruction method.
Her Inverse problem research incorporates themes from Algorithm, Jacobian matrix and determinant, Iterative reconstruction and Regularization. Her studies deal with areas such as Quantitative susceptibility mapping and Sensitivity as well as Algorithm. Her work in Tomography addresses issues such as Beam, which are connected to fields such as Beam hardening.
Her primary areas of investigation include Artificial intelligence, Deep learning, Tomography, Inverse problem and Electrical impedance tomography. Her Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Ultrasound, Computer vision and Pattern recognition. Jin Keun Seo works mostly in the field of Deep learning, limiting it down to concerns involving Image and, occasionally, Fourier transform, Poisson summation formula, Folding, Sampling and Divergence.
Her work investigates the relationship between Tomography and topics such as Image processing that intersect with problems in Jacobian matrix and determinant, Robustness, Iterative reconstruction and Uncertain data. Her Inverse problem research is multidisciplinary, relying on both Isotropy, Computational physics and Nyquist stability criterion. Jin Keun Seo performs integrative study on Electrical impedance tomography and Lung imaging.
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Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm
Ohin Kwon;Eung Je Woo;Jeong-Rock Yoon;Jin Keun Seo.
IEEE Transactions on Biomedical Engineering (2002)
Deep learning for undersampled MRI reconstruction.
Chang Min Hyun;Hwa Pyung Kim;Sung Min Lee;Sungchul Lee.
Physics in Medicine and Biology (2018)
Magnetic resonance electrical impedance tomography (MREIT) for high-resolution conductivity imaging
Eung Je Woo;Jin Keun Seo.
Physiological Measurement (2008)
Reconstruction of conductivity and current density images using only one component of magnetic field measurements
Jin Keun Seo;Jeong-Rock Yoon;Eung Je Woo;Ohin Kwon.
IEEE Transactions on Biomedical Engineering (2003)
Conductivity and current density image reconstruction using harmonic Bz algorithm in magnetic resonance electrical impedance tomography
Suk Hoon Oh;Byung Il Lee;Eung Je Woo;Soo Yeol Lee.
Physics in Medicine and Biology (2003)
Magnetic Resonance Electrical Impedance Tomography (MREIT)
Jin Keun Seo;Eung Je Woo.
Siam Review (2011)
The layer potential technique for the inverse conductivity problem
Hyeonbae Kang;Jin Keun Seo.
Inverse Problems (1996)
J-substitution algorithm in magnetic resonance electrical impedance tomography (MREIT): phantom experiments for static resistivity images
Hyun Soo Khang;Byung Il Lee;Suk Hoon Oh;Eung Je Woo.
IEEE Transactions on Medical Imaging (2002)
A real time algorithm for the location search of discontinuous conductivities with one measurement
Ohin Kwon;Jin Keun Seo;Jeong Rock Yoon.
Communications on Pure and Applied Mathematics (2002)
Level set-based bimodal segmentation with stationary global minimum
Suk-Ho Lee;Jin Keun Seo.
IEEE Transactions on Image Processing (2006)
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