• Journal of the Korean Society of Radiology
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• v.4 no.3
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• pp.13-18
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• 2010

Purpose : Diffusion tensor imaging(DTI) allows the visualization of fiber tract damage in patients with cerebral infarction. The purpose of this study is to evaluate the correlation between degree of NIH stoke scale and fractional anisotropy (FA) in patient with cerebral infarction. Material and Methods : 16 patients aged 36~77 years(male : 11, female : 5, mean age : 61y), diagnosed cerebral infarction by diffusion weighted imaging(DWI), underwent 24 directional diffusion tensor imaging(DTI). Patients had the DTI taken within 3days of stroke onset. Comparison of DWI, FA value on DTI were measured infarcted area and counter part of specific region of interest (ROI). And evaluation of differences between clinically improved patient group (n=9) and unimproved patient group (n=7) until 2 week follow up after development of cerebral infarction. Clinical status was scaled by NIH stroke scale. Results : Quantitative measurements of FA confirmed statistically the significant diffusion changes in the infarct compared with the matched-counter part region. In DWI, the infarcted area shows high signal intensity, however FA value on DTI was lower than normal brain parenchyma. The FA value of clinically improved patient by NIH stroke scale was 0.49, and the value of contralateral normal brain parenchyma was 0.41. On the contrary, FA value of infarcted area shows about 15% lower than normal brain parenchyma. But, the FA value of unimproved patient by NIH stroke scale represents a half those of contralateral normal brain parenchyma (0.28 on infarcted area vs. 0.56 on normal brain parenchyma). So, the FA value of unimproved patient group was considerably less than those of improved. Conclusion : It is concluded that the unimproved patient group after cerebral infarction showed much less FA value than that of normal brain parenchyma. The FA value of DTI may be one of the useful parameter to predict outcome of cerebral infarction patients.

• Investigative Magnetic Resonance Imaging
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• v.14 no.2
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• pp.126-133
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• 2010

Purpose : The study was to evaluate the localization of the abnormal gyral and sulcal patterns obtained by means of brain surface rendering imaging. Materials and Methods : Nineteen patients with cortical dysplasia who underwent brain surface rendering MR imaging were included in this study. We acquired MP-RAGE sequence and created the 3-D surface rendering MR images by using $VoxelPlus^{(R)}$. Anatomical locations and configurations of abnormal gyri and sulci were reviewed. Results : Abnormal gyral and sulcal patterns were seen 18 in 19 patients. The configuration and orientation of affected gyri and sulci were clearly evaluated in the brain surface rendering images. In a lissencephaly, the a cortex was not delineated and showed markedly thick and smooth gyral pattern. In a schizencephaly, there were wheel shaped broad gyral pattern around the cleft. In a hemimegalencephaly, an affected hemisphere were enlarged and displayed thick and wide gyral pattern. In CBPS, the insular cortex was exposed and the gyri of the lesion were thickened. In focal cortical dysplasia, there were irregular serrated or thick and enlarged gyri. Conclusion : Brain surface rendering MR imaging is useful for the evaluation of a detailed gyral pattern and accurate involvement site of abnormal gyri.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.455-458
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• 2010

Extracting the brain from magnetic resonance imaging head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool produces a brain mask which may be too smooth for practical use to reduce the accuracy. This paper presents a novel and indirect brain extraction method based on non-brain tissue segmentation. Based on ITK, the proposed method allows a non-brain contour by using region growing to match with the original image naturally and extract the brain tissue. Experiments on two set of MRI data and 2D brain image in horizontal plane and 3D brain model indicate successful extraction of brain tissue from a head.

• Anxiety and mood
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• v.3 no.2
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• pp.91-96
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• 2007

Panic disorder is a common psychiatric illness that causes considerable morbidity. However, the biological basis of panic disorder remains unclear. In this report, we present and summarize the current literature on functional neuroimaging studies related to the neurobiology of panic disorder. The findings were summarized and divided into six groups : (1) known brain structures related to anxiety, especially panic disorder ; (2) structural results ; (3) functional imaging studies at rest ; (4) functional imaging studies with challenge testing ; (5) neuroreceptor studies ; and (6) changes in the treatment of panic disorder. Based on the findings of these neuroimaging studies, it seems as though panic disorder involves the hippocampal and parahippocampal areas, including the amygdala, as well as some cortical regions, such as the temporal and prefrontal cortices. Panic disorder is known to be associated with an imbalance between the right and left hemispheres of the brain at rest or during panic attacks. During a panic attack, patients with panic disorder are likely to experience an increase in local activity in the cingulate, insula, midbrain, and so on. On the other hand, a widespread reduction in the cortical areas has also been reported in most provocation studies. Thus, panic disorder may be related to the excess activation of the fear networks in response to subtle environmental cues and insufficient inhibition from higher cortical control areas ; however ; further studies are recommended in order to fully understand the neurobiology of panic disorder.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.123-128
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• 2005

Magnetoencephalography (MEG) is the measurement of the magnetic fields produced by electrical activity in the brain, usually conducted externally, using extremely sensitive devices such as Superconducting Quantum Interference Device (SQUID). MEG needs complex and expensive measurement settings. Because the magnetic signals emitted by the brain are on the order of a few femtoteslas (1 fT = 10-15T), shielding from external magnetic signals, including the Earth's magnetic field, is necessary. An appropriate magnetically shielded room is very expensive, and constitutes the bulk of the expense of an MEG system. MEG is a relatively new technique that promises good spatial resolution and extremely high temporal resolution, thus complementing other brain activity measurement techniques such as electroencephalography (EEG), positron emission tomography (PET), single-photon emission computed tomography (SPECT) and functional magnetic resonance imaging (fMRI). MEG combines functional information from magnetic field recordings with structural information from MRI. The clinical uses of MEG are in detecting and localizing epileptic form spiking activity in patients with epilepsy, and in localizing eloquent cortex for surgical planning in patients with brain tumors. Magnetoencephalography may be used alone or together with electroencephalography, for the measurement of spontaneous or evoked activity, and for research or clinical purposes.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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• pp.101-108
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• 2015

Recent breakthroughs in functional neuroimaging techniques have launched the quest of mapping the connections of the human brain, otherwise known as the human connectome. Imaging connectomics is an umbrella term that refers to the neuroimaging techniques used to generate these maps, which recently has enabled comprehensive brain mapping of network connectivity combined with graph theoretic methods. In this review, we present an overview of the key concepts in functional connectomics. Furthermore, we discuss articles that applied task-based and/or resting-state functional magnetic resonance imaging to examine network deficits in post-traumatic stress disorder (PTSD). These studies have provided important insights regarding the etiology of PTSD, as well as the overall organization of the brain network. Advances in functional connectomics are expected to provide insight into the pathophysiology and the development of biomarkers for diagnosis and treatment of PTSD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.73-88
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• 2023

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.51.2-51.2
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• 2015

Understanding interfacial phenomena has been one of the main research issues not only in semiconductors but only in life sciences. I have been trying to meet the atomic scale surface and interface analysis challenges from semiconductor industries and furthermore to extend the application scope to biomedical areas. Optical imaing has been most widely and successfully used for biomedical imaging but complementary ion beam imaging techniques based on mass spectrometry and ion scattering can provide more detailed molecular specific and nanoscale information In this presentation, I will review the 27 years history of medium energy ion scattering (MEIS) development at KRISS and DGIST for nanoanalysis. A electrostatic MEIS system constructed at KRISS after the FOM, Netherland design had been successfully applied for the gate oxide analysis and quantitative surface analysis. Recenlty, we developed time-of-flight (TOF) MEIS system, for the first time in the world. With TOF-MEIS, we reported quantitative compositional profiling with single atomic layer resolution for 0.5~3 nm CdSe/ZnS conjugated QDs and ultra shallow junctions and FINFET's of As implanted Si. With this new TOF-MEIS nano analysis technique, details of nano-structured materials could be measured quantitatively. Progresses in TOF-MEIS analysis in various nano & bio technology will be discussed. For last 10 years, I have been trying to develop multimodal nanobio imaging techniques for cardiovascular and brain tissues. Firstly, in atherosclerotic plaque imaging, using, coherent anti-stokes raman scattering (CARS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) multimodal analysis showed that increased cholesterol palmitate may contribute to the formation of a necrotic core by increasing cell death. Secondly, surface plasmon resonance imaging ellipsometry (SPRIE) was developed for cell biointerface imaging of cell adhesion, migration, and infiltration dynamics for HUVEC, CASMC, and T cells. Thirdly, we developed an ambient mass spectrometric imaging system for live cells and tissues. Preliminary results on mouse brain hippocampus and hypotahlamus will be presented. In conclusions, multimodal optical and mass spectrometric imaging privides overall structural and morphological information with complementary molecular specific information, which can be a useful methodology for biomedical studies. Future challenges in optical and mass spectrometric imaging for new biomedical applications will be discussed.

• Brain Tumor Research and Treatment
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• v.6 no.2
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• pp.47-53
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• 2018

Brain tumors represent a diverse spectrum of histology, biology, prognosis, and treatment options. Although MRI remains the gold standard for morphological tumor characterization, positron emission tomography (PET) can play a critical role in evaluating disease status. This article focuses on the use of PET with radiolabeled glucose and amino acid analogs to aid in the diagnosis of tumors and differentiate between recurrent tumors and radiation necrosis. The most widely used tracer is $^{18}F$-fluorodeoxyglucose (FDG). Although the intensity of FDG uptake is clearly associated with tumor grade, the exact role of FDG PET imaging remains debatable. Additionally, high uptake of FDG in normal grey matter limits its use in some low-grade tumors that may not be visualized. Because of their potential to overcome the limitation of FDG PET of brain tumors, $^{11}C$-methionine and $^{18}F$-3,4-dihydroxyphenylalanine (FDOPA) have been proposed. Low accumulation of amino acid tracers in normal brains allows the detection of low-grade gliomas and facilitates more precise tumor delineation. These amino acid tracers have higher sensitivity and specificity for detecting brain tumors and differentiating recurrent tumors from post-therapeutic changes. FDG and amino acid tracers may be complementary, and both may be required for assessment of an individual patient. Additional tracers for brain tumor imaging are currently under development. Combinations of different tracers might provide more in-depth information about tumor characteristics, and current limitations may thus be overcome in the near future. PET with various tracers including FDG, $^{11}C$-methionine, and FDOPA has improved the management of patients with brain tumors. To evaluate the exact value of PET, however, additional prospective large sample studies are needed.