Creating innovative bio-convergent technologies for better human life

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Magnetic Resonance Imaging Laboratory
Sung-Hong Park

Associate Professor

Broad research interests of magnetic resonance imaging (MRI) laboratory at Korea Advanced Institute of Science and Technology (KAIST) lie in (i) developing new MRI techniques of image acquisition, processing, and analysis, (ii) unveiling the inter-regional brain communication mechanism, (iii) applying machine learning algorithms to MRI databases and MRI artifact suppression, and (iv) developing intraoperative MRI devices, These technical developments can make big differences in understanding baseline and functional brain physiology, and also diagnosis / prognosis / treatment of our body.

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Research Area

1) Anatomical/Physiological/Functional MRI
Noninvasive visualization of blood vessels, blood flow, and blood oxygenation level dependence is an important step toward understanding sources of functional brain signals and brain diseases. We are developing methods to image both arteries and veins within a single MRI acquisition without compromising their image qualities, map blood flow in brain, retina, and kidney noninvasively, and map brain function with high spatial resolutions. These techniques have been used to understand signal sources of high-resolution functional MRI and also vascular origin of brain diseases such as brain tumors and stroke

2) Neuronal Current MRI
Our brain requires coherent information flow for inter-regional communication. However, it is still not clear how a neuronal group selects its communication target. We are trying to develop a new fMRI technique that enables us to detect neuronal currents directly rather than the conventional fMRI relying on secondary hemodynamic responses. We try to use the new fMRI technique as well as systems biological approaches for better understanding of the inter-regional brain communication mechanism and use EEG to prove the concept.
 

3) Machine Learning for Biomedical Imaging
Machine learning has been of interest to the many research areas and one of the hottest applications is biomedical imaging. We are trying to develop and apply machine learning algorithms for MRI artifacts suppression, denoising, fast MRI data acquisition and analysis, and diagnosis/prognosis/treatment monitoring of various diseases.

4) Intraoperative MRI
Intraoperative MRI is application of MR imaging during or in-between medical treatments. We are developing system for real-time imaging, display, and segmentation as well as real-time tracking of operative devices within MRI

5) Biomedical Applications
We have tried to integrate all these imaging techniques for multi-parametric assessment of various brain diseases. We believe combination of the various aspects of the brain would provide much more accurate information than conventional approaches. Also we expect this approach can stimulate interdisciplinary collaborative studies involving engineers, neuroscientists, and clinicians.

Key Achievements
  • 1. Sung-Hong Park, Tae Kim, Ping Wang, and Seong-Gi Kim. Sensitivity and specificity of high-resolution balanced steady state free precession fMRI at high field of 9.4T. NeuroImage 2011, Sep;58(1):168-176
  • 2. Sung-Hong Park and Timothy Q. Duong. Brain MR Perfusion-Weighted Imaging with Alternate Ascending/ Descending Directional Navigation. Magnetic Resonance in Medicine, 2011, Jun;65(6):1578-1591.
  • 3. Sung-Hong Park and Timothy Q. Duong. Alternate Ascending/Descending Directional Navigation Approach for Imaging Magnetization Transfer Asymmetry. Magnetic Resonance in Medicine, 2011, Jun;65(6):1702-1710.
  • 4. Sung-Hong Park, Chan-Hong Moon, and Kyongtae Ty Bae. Compatible Dual-Echo Arteriovenography (CODEA) using an echo-specific K-space reordering scheme. Magnetic Resonance in Medicine, 2009, Apr;61(4):767-774.
  • 5. Sung-Hong Park, Kazuto Masamoto, Kristy Hendrich, Iwao Kanno, and Seong-Gi Kim. Imaging brain vasculature with BOLD microscopy: MR detection limits determined by in vivo two-photon microscopy. Magnetic Resonance in Medicine, 2008, Apr;59(4):855-865.

Achievement In This Year

  • 1. Paul Kyu Han, Seung Hong Choi, and Sung-Hong Park. Investigation of Control Scans in Pseudo-Continuous Arterial Spin Labeling (pCASL): Strategies for Improving Sensitivity and Reliability of pCASL, Magnetic Resonance in Medicine, 2017;78(3):917-929
  • 2. Hyun-Soo Lee, Seung Hong Choi, and Sung-Hong Park. Single and Double Acquisition Strategies for Compensation of Artifacts from Eddy Current and Transient Oscillation in Balanced Steady-State Free Precession, Magnetic Resonance in Medicine, 2017;78(1);254-263
  • 3. Kyong Hwan Jin, Ji-Yong Um, Dongwook Lee, Juyoung Lee, Sung-Hong Park and Jong Chul Ye. MRI artifact correction using sparse + low-rank decomposition of annihilating filter-based Hankel matrix, Magnetic Resonance in Medicine, 2017;78(1):327-340
  • 4. Jae-Woong Kim, Seong-Gi Kim, and Sung-Hong Park. Phase Imaging with Multiple Phase-Cycled Balanced Steady-State Free Precession at 9.4T. NMR in Biomedicine, 2017;30(6):e3699
  • 5. Ki Hwan Kim and Sung-Hong Park. Artificial Neural Network for Suppression of Banding Artifacts in Balanced Steady-State Free Precession MRI, Magnetic Resonance Imaging, 2017;37:139-146
  • 6. Paul Kyu Han, HyunWook Park, and Sung-Hong Park. DC Artifact Correction for Arbitrary Phase-Cycling Sequence. Magnetic Resonance Imaging, 2017;38:21-26
  • 7. Dongwook Lee, Kyong Hwan Jin, Eung-Yeop Kim, Sung-Hong Park, and Jong Chul Ye, Acceleration of MR Parameter Mapping Using Annihilating Filter-Based Low Rank Hankel Matrix (ALOHA). Magnetic Resonance in Medicine, 2016;76(6):1848-1864.