D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 45 Citations 7,105 234 World Ranking 2668 National Ranking 44

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Mathematical analysis
  • Statistics

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.

Her most cited work include:

  • Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm (261 citations)
  • Reconstruction of conductivity and current density images using only one component of magnetic field measurements (178 citations)
  • Magnetic resonance electrical impedance tomography (MREIT) for high-resolution conductivity imaging (176 citations)

What are the main themes of her work throughout her whole career to date?

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.

She most often published in these fields:

  • Electrical impedance tomography (26.69%)
  • Artificial intelligence (22.97%)
  • Inverse problem (18.92%)

What were the highlights of her more recent work (between 2015-2021)?

  • Artificial intelligence (22.97%)
  • Deep learning (8.45%)
  • Electrical impedance tomography (26.69%)

In recent papers she was focusing on the following fields of study:

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.

Between 2015 and 2021, her most popular works were:

  • Deep learning for undersampled MRI reconstruction. (148 citations)
  • Metal Artifact Reduction for Polychromatic X-ray CT Based on a Beam-Hardening Corrector (42 citations)
  • Spectroscopic imaging of a dilute cell suspension (37 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mathematical analysis
  • Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

355 Citations

Deep learning for undersampled MRI reconstruction.

Chang Min Hyun;Hwa Pyung Kim;Sung Min Lee;Sungchul Lee.
Physics in Medicine and Biology (2018)

276 Citations

Magnetic resonance electrical impedance tomography (MREIT) for high-resolution conductivity imaging

Eung Je Woo;Jin Keun Seo.
Physiological Measurement (2008)

259 Citations

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)

248 Citations

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)

242 Citations

Magnetic Resonance Electrical Impedance Tomography (MREIT)

Jin Keun Seo;Eung Je Woo.
Siam Review (2011)

188 Citations

The layer potential technique for the inverse conductivity problem

Hyeonbae Kang;Jin Keun Seo.
Inverse Problems (1996)

161 Citations

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)

156 Citations

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)

136 Citations

Level set-based bimodal segmentation with stationary global minimum

Suk-Ho Lee;Jin Keun Seo.
IEEE Transactions on Image Processing (2006)

135 Citations

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