University-Industry Cooperation Group of Kyung Hee University

Yonsei University

Kohwang Foundation, Kohwang Board of Trustee

Woo, Eung Je

Kwon, Oh in

Seo, Jin Keun

Lee, Byung Il

Lee, Suk Ho

Kim, Tae Seong

Jin Keun Seo

Eung Je Woo

Oh In Kwon

Suk Ho Lee

Sung Chul Lee

Sung Min Lee

Chang Min Hyun

Hwa Pyung Kim

Byung Ii Lee

Ohin Kwon

Tae Seong Kim

Dong In Oh

Byung Il Lee

An under-sampling apparatus for MR image reconstruction by using machine learning and a method thereof, an MR image reconstruction device by using machine learning and a method thereof, and a recoding medium thereof are disclosed. The disclosed under-smapling apparatus includes: a setting portion that sets a region corresponding to a center of the k-space image as a first region and remaining regions as a second region; and an under-sampling portion that full-samples the first region and under-samples the second region, wherein in the under-sampling performed in the second region, lines are selected at regular intervals and then only the selected line is full-sampled. According to the under-sampling apparatus, a high-resolution MR image can be acquired while reducing imaing time.

Apparatus and method for reconstructing magnetic resonance image using learning, and under-sampling apparatus method and recording medium thereof

A system for electrical impedance tomography and method thereof are disclosed, by which electrical characteristics within a measurement target can be precisely detected. The present invention includes the steps of injection a current to a measurement target via at least one electrode pair selected form a plurality of electrodes () attached to the measurement target, detecting voltage of a surface of the measurement target using a plurality of voltmeters () connected to the electrodes that are not selected, respectively, adjusting gains of the voltmeters according to maximum values of the detected voltages, respectively, amplifying the detected voltages using the gain-adjusted voltmeters, respectively, and imaging an internal part of the measurement target based on the amplified voltages.

System for electrical impedance tomography and method thereof

System for visualizing conductivity and current density distributions including a plurality of current injecting devices () for injecting currents into a measuring object; a measuring unit() for measuring a magnetic flux density due to the currents injected into a measuring object; an operating unit() for selecting one pair of the current injecting devices in succession so as to inject currents of different directions into the measuring object, and calculating directional components of an anisotropic conductivity inside of the measuring object on the basis of the measured magnetic flux density; and displaying means for visualizing an inside of the measuring object by using the calculated directional components of the anisotropic conductivity.

System and method for visualizing conductive and current density distribution in object

System for visualizing conductivity and current density distributions including a plurality of current injecting devices for injecting currents into a measuring object, an MRI scanner for measuring one directional component of a magnetic flux density due to each of the currents injected into a measuring object, an operating part for controlling the current injecting devices so as to inject currents of different directions into the measuring object, and calculating a conductivity distribution and a current density distribution inside of the measuring object by using the one directional component of a magnetic flux density, and displaying means for visualizing the conductivity and current density distributions calculated by the operating part, thereby permitting to visualize the conductivity and the current density of the measuring object more accurately.

System and method for three-dimensional visualization of conductivity and current density distribution in electrically conducting object

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Seoul, South Korea

Jin Keun Seo