• Journal of Magnetics
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• v.21 no.2
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• pp.281-285
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• 2016

The purpose of study was to evaluate SNR and CNR with different contrast agent contents (1.0 mmol/mL gadobutrol and 0.5 mmol/mL gadoterate meglumine) for spin echo (SE) and 3-dimension contrast-enhanced fast field echo (3D CE-FFE) pulse sequences. In this study, we compared the SNR and the CNR between 0.5 mmol/mL gadoterate meglumine and 1.0 mmol/mL gadobutrol according to the concentration of contrast agent in brain MRI. When we compared between SE and 3D CE-FFE pulse sequences, the higher SNR and CNR using 3D CE-FFE pulse sequence can be acquire regardless of contrast agent contents. Also, a statistically significant difference was found for SNR and CNR between all protocols. In conclusion, our results demonstrated that the SNR and CNR have not risen proportionately with contrast agent contents. We hope that these results presented in this paper will contribute to decide contrast agent contents for brain MRI.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2425-2431
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• 1997

The increased usage of Magnetic Resonance Image (MRI) required the method for automatic segmentation of medical image that is more useful so as to diagnose the dissecitive information of a atient quickly and effectively through MR scans.The use of neural networks may give much hep to solving the complex problems concerned the matter. This paper proposes the new method for automatic segmentation of magnetic resonance (MR) images of the brain by using neural networks brained by back-propagation algorithm. The trained neural networks by the segmenting MR images of a patient produce an output that networks can segment MR images of the other patients automatically, too and show a clear image of the brain.

• Journal of Korean Neurosurgical Society
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• v.37 no.6
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• pp.453-455
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• 2005

Two young women were brought to the Emergency room with generalized tonic and clonic seizures. Seizure developed seven and ten days after delivery respectively without the clinical signs of pre-eclampsia throughout the pregnancies. Magnetic resonance(MR) image of the brain showed characteristically symmetrical abnormal signals in the parietal and occipital regions. After several days of medical treatment, they were discharged without neurologic sequelae and follow-up MR images taken three months after discharge showed complete disappearance of the previous abnormal signals.

• Investigative Magnetic Resonance Imaging
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• v.17 no.3
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• pp.169-180
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• 2013

Purpose : The purpose of this study was to setup a concuurent transcranial magnetic stimulation (TMS)-functional MRI (fMRI) system for understanding causality of the functional brain network. Materials and Methods: We manufactured a TMS coil holder using nonmagnetic polyether ether ketone (PEEK). We simulated magnetic field distributions in the MR scanner according to TMS coil positions and angles. To minimize image distortions caused by TMS application, we controlled fMRI acquisition and TMS sequences to trigger TMS during inter-volume intervals. Results: Simulation showed that the magnetic field below the center of the coil was dramatically decreased with distance. Through the MR phantom study, we confirmed that TMS application around inter-volume acquisition time = 100 miliseconds reduced imaging distortion. Finally, the applicability of the concurrent TMS-fMRI was tested in preliminary studies with a healthy subject conducting a motor task within TMS-fMRI and passive motor movement induced by TMS in fMRI. Conclusion: In this study, we confirmed that the developed system allows use of TMS inside an fMRI system, which would contribute to the research of brain activation changes and causality in brain connectivity.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.341-344
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• 2002

Neurodegenerative disorders, like Alzheimer's disease, are often accompanied by reduced brain perfusion (cerebral blood flow). Using the intrinsic magnetic properties of water, arterial spin labeling magnetic resonance imaging (ASLMRI) can map brain perfusion without injection of radioactive tracers or contrast agents. However, accuracy in measuring perfusion with ASL-MRI can be limited because of contributions to the signal from stationary spins and because of signal modulations due to transient magnetic field effects. The goal was to optimize ASL-MRI for perfusion measurements in the aging human brain, including brains with Alzheimer's disease. A new ASL-MRI sequence was designed and evaluated on phantom and humans. Image texture analysis was performed to test quantitatively improvements. Compared to other ASL-MRI methods, the newly designed sequence provided improved signal to noise ratio improved signal uniformity across slices, and thus, increased measurement reliability. This new ASL-MRI sequence should therefore provide improved measurements of regional changes of brain perfusion in normal aging and neurodegenerative disorders.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.55-66
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• 2012

Purpose : During brain MRI scanning, subject's head motion can adversely affect MRI images. To minimize MR image distortion by head movement, we developed an optical tracking system to detect the 3-D movement of subjects. Materials and Methods: The system consisted of 2 CCD cameras, two infrared illuminators, reflective sphere-type markers, and frame grabber with desktop PC. Using calibration which is the procedure to calculate intrinsic/extrinsic parameters of each camera and triangulation, the system was desiged to detect 3-D coordinates of subject's head movement. We evaluated the accuracy of 3-D position of reflective markers on both test board and the real MRI scans. Results: The stereo system computed the 3-D position of markers accurately for the test board and for the subject with glasses with attached optical reflective marker, required to make regular head motion during MRI scanning. This head motion tracking didn't affect the resulting MR images even in the environment varying magnetic gradient and several RF pulses. Conclusion: This system has an advantage to detect subject's head motion in real-time. Using the developed system, MRI operator is able to determine whether he/she should stop or intervene in MRI acquisition to prevent more image distortions.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.345-352
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• 2016

The purpose of this study was to needed basis of measure MRI CAD development for signal to noise ratio (SNR) by pulse sequence analysis from region of interest (ROI) in brain magnetic resonance imaging (MRI) contrast. We examined images of brain MRI contrast enhancement of 117 patients, from January 2005 to December 2015 in a University-affiliated hospital, Seoul, Korea. Diagnosed as one of two brain diseases such as meningioma and cysts SNR for each patient's image of brain MRI were calculated by using Image J. Differences of SNR among two brain diseases were tested by SPSS Statistics21 ANOVA test for there was statistical significance (p < 0.05). We have analysis socio-demographical variables, SNR according to sequence disease, 95% confidence according to SNR of sequence and difference in a mean of SNR. Meningioma results, with the quality of distributions in the order of T1CE, T2 and T1, FLAIR. Cysts results, with the quality of distributions in the order of T2 and T1, T1CE and FLAIR. SNR of MRI sequences of the brain would be useful to classify disease. Therefore, this study will contribute to evaluate brain diseases, and be a fundamental to enhancing the accuracy of CAD development.

• Journal of Radiation Industry
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• v.11 no.3
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• pp.139-144
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• 2017

To evaluation of clinical usefulness for B1+RMS limits, we compared image quality of Routine, Specific absorption rate (SAR) and Root mean square (RMS) protocol. 5 volunteers underwent Magnetic Resonance Imaging (MRI) scan of the brain using three different protocols. We draw Region of interest ROI in cortex, white matter, gray matter, putamen and thalamus of axial plan. Signal to noise ratio (SNR) were evaluated in each area and Contrast to noise ration (CNR) were evaluated between white matter and gray matter. Qualitative evaluation was used to score each ROI. B1+RMS is confirmed its usefulness compared to conventional SAR standard on the aspect of improvement of image quality, reduction of scan time and easy adjusting parameter.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.