J
Jin Keun Seo
Researcher at Yonsei University
Publications - 279
Citations - 7701
Jin Keun Seo is an academic researcher from Yonsei University. The author has contributed to research in topics: Electrical impedance tomography & Iterative reconstruction. The author has an hindex of 44, co-authored 272 publications receiving 6585 citations. Previous affiliations of Jin Keun Seo include Kyung Hee University & University of Minnesota.
Papers
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Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm
TL;DR: A new image reconstruction algorithm called J-substitution algorithm produces cross-sectional static images of resistivity (or conductivity) distributions that are comparable to that of MRI.
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Deep learning for undersampled MRI reconstruction.
TL;DR: In this article, a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies is presented. But the method is not suitable for image folding.
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Deep learning for undersampled MRI reconstruction
TL;DR: A deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well is presented.
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Magnetic resonance electrical impedance tomography (MREIT) for high-resolution conductivity imaging
Eung Je Woo,Jin Keun Seo +1 more
TL;DR: This paper reviews MREIT from the basics to the most recent research outcomes, focusing on measurement techniques and experimental methods rather than mathematical issues, and summarizes what has been done and what needs to be done.
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Reconstruction of conductivity and current density images using only one component of magnetic field measurements
TL;DR: This paper proposes a way to eliminate the requirement of subject rotation by careful mathematical analysis of the MRCDI problem, which needs to measure only one component of the induced magnetic flux density and reconstruct both cross-sectional conductivity and current density images without any subject rotation.