• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.47-54
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• 2012

Purpose : This study was performed to evaluate the characteristics of rat mesenchymal stem cells (RMSCs) transduced with human ferritin gene and investigate $in$ $vitro$ MRI detectability of ferritin-transduced RMSCs. Materials and Methods: The RMSCs expressing both myc-tagged human ferritin heavy chain subunit (myc-FTH) and green fluorescence protein (GFP) were transduced with lentiviurs. Transduced cells were sorted by GFP expression using a fluorescence-activated cell sorter. Myc-FTH and GFP expression in transduced cells were detected by immunofluorescence staining. The cell proliferative ability and viability were assessed by MTT assay. The RMSC surface markers (CD29+/CD45-) were analyzed by flow cytometry. The intracellular iron amount was measured spectrophotometically and the presence of ferritin-iron accumulation was detected by Prussian blue staining. $In$ $vitro$ magnetic resonance imaging (MRI) study of cell phantoms was done on 9.4 T MR scanner to evaluate the feasibility of imaging the ferritin-transduced RMSCs. Results: The myc-FTH and GFP genes were stably transduced into RMSCs. No significant differences were observed in terms of biologic properties in transduced RMSCs compared with non-transduced RMSCs. Ferritin-transduced RMSCs exhibited increased iron accumulation ability and showed significantly lower $T_2$ relaxation time than non-transduced RMSCs. Conclusion: Ferritin gene as MR reporter gene could be used for non-invasive tracking and visualization of therapeutic mesenchymal stem cells by MRI.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.253-259
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• 2015

The information of contrast media concentration on target organ is very important to get reduce the side effect and high contrast imaging. We investigated alternation of signal intensity as a function of the modality of Gd-based contrast media on spin echo and ultra short time echo (UTE) of T1 effective pulse sequence at 3T MRI unit. Gadoxetic acid, which is a MRI T1 contrast medium, was used to manufacture an agarose phantom diluted in various molarities, and sterile water and agarose 2% were used as the buffer solution for the dilution. The gold standard T1 calculation was based on coronal single section imaging of the phantom mid-point with 2D Inversion recovery spine-echo pulse sequence MR imaging for testing of phantom accuracy. The 1-2mmol/L and 7mmol/L was shown the maximum signal intensity on spin echo and UTE respectively. We confirm the difference of contrast media concentration which was shown the maximum signal intensity depending on the T1 effective pulse sequence.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.651-655
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• 2008

It provides a three-dimensional virtual experience consisting of information presented to and perceived by the senses of the user. Recently, the advent of a virtual avatar, which mimics the appearance and behavior of humans, enable that the virtual reality (VR) can provide not only a virtual space but also a virtual society to be interact with a virtual avatar which represents humans. But, it is impossible that the user directly interacts with the VE in previous studies, though direct interactions occur in real social relationship. Therefore, In this study, we know that the cognition about a avatar when the user directly interacts with the virtual avatar in the VE. In order to investigate this purpose, we performed a fMRI study using VE that a avatar accept or reject the user's offer when the user offer his (or her) hand to a avatar. In result of questionnaire about the user's feeling by avatar's action, the user feels about avatar's acceptance action that the avatar acts positively and suitably. In contrast the user feels about avatar's rejective action that the avatar acts negatively and disapprovingly. In result of fMRI analysis, the primary visual area, the visual association area, the SMA, the premotor area, the cerebellum and etc, activate in common with the other avatar's acceptance action and rejective action. The results show that the subject recognizes not only avatars as social objects but also avatar's action as socially meaningful action. In other words, it is possible to transfer social context and emotion through avatar's action.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.

• Journal of the Korean Society of Radiology
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• v.9 no.5
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• pp.307-314
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• 2015

This study was conducted in reducing the involuntary motion artifacts because of lungs and heart movements as well as the aliasing artifacts generated during the use of the reduced-FOV EPI technique while performing breast MRI. Performed on a total of 38 obesity female subjects who visited the clinic for pre-examination before surgery within the period from August 1 to November 30, 2014. The 3.0T MRI scanner equipped with a breast scanning coil. Qualitative and quantitative analyses were each used for the evaluation of the acquired images while an Paired T-test and Wilcoxon rank test were performed to check the statistical significance. The variation ratio rose by 15.69% with the additional application of a pre-saturation pulse in the lesion, by 13.72% near the lesion, and 20.63% in the fat and the contrast-to-noise ratio rose by 10.58% in and near the lesion and by 12.03% in the lesion and fat, respectively. there were increases of 22.05% and 21.42% at 0 and 1000 respectively in qulitative evaluation and growth of 16.10% in apparent diffusion coefficient. it showed a statistically significant result(p<0.05) in signal to noise ratio, contrast to noise ratio, diffusion slope coefficient and apparent diffusion coefficient. The involuntary movements artifacts that occur in the phase encoding direction and the aliasing artifacts are considered to be reduced to obtain the best image in the additional use of the pre-saturation pulse as DWI is acquired.

• Investigative Magnetic Resonance Imaging
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• v.2 no.1
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• pp.58-66
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• 1998

Purpose : This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. Material and Methods : The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier. Results : MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is $E^{3}$ space rather thatn the conventional $E^{2}$ space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification. Conclusion : We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.135-144
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• 2024

Ultrasonography examination has limitations in quantifying hepatic fat quantification. Therefore, this study aimed to experimentally demonstrate whether changes in signal attenuation during ultrasound imaging can be quantified using simulated hepatic phantoms to assess hepatic fat content. Additionally, we aimed to evaluate the potential of ultrasound imaging for diagnosing hepatic fatty liver by analyzing the relationship between hepatic fat content and signal intensity in ultrasound images. In this study, we developed a total of five stimulated hepatic phantoms by homogeneously mixing water and oil. We confirmed the fat content of the phantoms using magnetic resonance imaging (MRI) and ultrasound imaging, and measured signal intensity according to distance in ultrasound images to analyze the correlation and mean comparison between fat content and signal intensity. We observed that as the fat content increased, the ultrasound penetration intensity decreased, confirming the potential for quantifying hepatic fat content using ultrasound. Additionally, the analysis of the correlation between the measured fat content using MRI and the signal intensity measured in ultrasound images showed a high correlation. Statistical analysis in our study confirmed that as the fat content increased, the slope representing signal during ultrasound imaging (US-GRE) decreased. In this study, it was statistically confirmed that the US-GRE value of ultrasound images gradually decreases as the fat content increases, and it is believed that US-GRE can serve as a biomarker expressing fatty liver content.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.12-16
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• 2015

Purpose Principal component analysis (PCA) is a method often used in the neuroimagre analysis as a multivariate analysis technique for describing the structure of high dimensional correlation as the structure of lower dimensional space. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly independent variables called principal components. In this study, in order to investigate the usefulness of PCA in the brain PET image analysis, we tried to analyze C[11]-PIB PET image as a representative case. Materials and Methods Nineteen subjects were included in this study (normal = 9, AD/MCI = 10). For C[11]-PIB, PET scan were acquired for 20 min starting 40 min after intravenous injection of 9.6 MBq/kg C[11]-PIB. All emission recordings were acquired with the Biograph 6 Hi-Rez (Siemens-CTI, Knoxville, TN) in three-dimensional acquisition mode. Transmission map for attenuation-correction was acquired using the CT emission scans (130 kVp, 240 mA). Standardized uptake values (SUVs) of C[11]-PIB calculated from PET/CT. In normal subjects, 3T MRI T1-weighted images were obtained to create a C[11]-PIB template. Spatial normalization and smoothing were conducted as a pre-processing for PCA using SPM8 and PCA was conducted using Matlab2012b. Results Through the PCA, we obtained linearly uncorrelated independent principal component images. Principal component images obtained through the PCA can simplify the variation of whole C[11]-PIB images into several principal components including the variation of neocortex and white matter and the variation of deep brain structure such as pons. Conclusion PCA is useful to analyze and extract the main pattern of C[11]-PIB image. PCA, as a method of multivariate analysis, might be useful for pattern recognition of neuroimages such as FDG-PET or fMRI as well as C[11]-PIB image.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.183-185
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• 2009

As imaging technology develops, magnetic resonance imaging (MRI) techniques have contributed to the understanding of brain function by providing anatomical structure of the brain and functional imaging related to information processing. Manganese-enhanced MRI (MEMRI) techniques can provide useful information about functions of the nervous system. However, systematic studies regarding information processing of pain have not been conducted. The purpose of this study was to detect brain activation during painful electrical stimulation using MEMRI with high spatial resolution. Male Sprague-Dawley rats (250-300 g) were divided into 3 groups: normal control, sham stimulation, and electric stimulation. Rats were anesthetized with 2.5% isoflurane for surgery. Polyethylene catheter (PE-10) was placed in the external carotid artery to administrate mannitol and MnCl2. The blood brain barrier (BBB) was broken by 20% D-mannitol under anesthesia mixed with urethane and a-chloralose. The hind limb was electrically stimulated with a 2Hz (10V) frequency while MnCl2 was infused. Brain activation induced by electrical stimulation was detected using a 4.7 T MRI. Remarkable signal enhancement was observed in the primary sensory that corresponds to sensory tactile stimulation at the hind limb region. These results suggest that signal enhancement is related to functional activation following electrical stimulation of the peripheral receptive field.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.