• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.93-99
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• 2003

The different perceiving patterns of each picture, alphabetic words and Chinese characters, were widely investigated psychophysically. The more precise localisation can be done in terms of brain activity us-ing functional image technique such as PET and fMRI recently, Until now, there was no fMRI study to make direct comparison between perception of single Chinese character and simplified pictures (pictograph). We have made direct comparison of these two components using modern magnetic resonance techniques. We cannot confirm the right hemispheric dominance for perception of single Chinese character and pictographs. These two kinds of perceiving pattern can be underlying different mechanism.

• Investigative Magnetic Resonance Imaging
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• v.11 no.2
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• pp.110-118
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• 2007

Purpose: The objective of this work to construct eigenvalue maps that have information of magnitude of three primary diffusion directions using diffusion tensor images. Materials and Methods: To construct eigenvalue maps, we used a 3.0T MRI scanner. We also compared the Moore-Penrose pseudo-inverse matrix method and the SVD (single value decomposition) method to calculate magnitude of three primary diffusion directions. Eigenvalue maps were constructed by calculating of magnitude of three primary diffusion directions. We did investigate the relationship between eigenvalue maps and fractional anisotropy map. Results: Using Diffusion Tensor Images by diffusion tensor imaging sequence, we did construct eigenvalue maps of three primary diffusion directions. Comparison between eigenvalue maps and Fractional Anisotropy map shows what is difference of Fractional Anisotropy value in brain anatomy. Furthermore, through the simulation of variable eigenvalues, we confirmed changes of Fractional Anisotropy values by variable eigenvalues. And Fractional anisotropy was not determined by magnitude of each primary diffusion direction, but it was determined by combination of each primary diffusion direction. Conclusion: By construction of eigenvalue maps, we can confirm what is the reason of fractional anisotropy variation by measurement the magnitude of three primary diffusion directions on lesion of brain white matter, using eigenvalue maps and fractional anisotropy map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.67-75
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• 2015

Purpose: To investigate the value of image post-processing software (FreeSurfer, IBASPM [individual brain atlases using statistical parametric mapping software]) and inversion time (TI) in volumetric analyses of the hippocampus and to identify differences in comparison with manual tracing. Materials and Methods: Brain images from 12 normal adults were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) with a slice thickness of 1.3 mm and TI of 800, 900, 1000, and 1100 ms. Hippocampal volumes were measured using FreeSurfer, IBASPM and manual tracing. Statistical differences were examined using correlation analyses accounting for spatial interpretations percent volume overlap and percent volume difference. Results: FreeSurfer revealed a maximum percent volume overlap and maximum percent volume difference at TI = 800 ms ($77.1{\pm}2.9%$) and TI = 1100 ms ($13.1{\pm}2.1%$), respectively. The respective values for IBASPM were TI = 1100 ms ($55.3{\pm}9.1%$) and TI = 800 ms ($43.1{\pm}10.7%$). FreeSurfer presented a higher correlation than IBASPM but it was not statistically significant. Conclusion: FreeSurfer performed better in volumetric determination than IBASPM. Given the subjective nature of manual tracing, automated image acquisition and analysis image is accurate and preferable.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.377-384
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• 2001

Generally. there have been limitations to investigate structural brain abnormalities with MR images for psychiatric patients. such as schizophrenia. depression and autism, since the brain abnormalities of psychiatric Patients are too small to be detected easily. It has been suggested to exploit the result of size-comparison or analysis of specified Part in various brain tissues. Results of brain analysis highly depend on accuracy of the brain segmentation because it is hard to segment image that the boundary between tissues in the brain MRI is inherently value. In this Paper. we improve the quality of brain segmentation so that we increase the credit of brain analysis. In addition, we Provide the improved images for studying brain abnormalities through left-right insular volume measure using handy software tool .

• Journal of Korean Neurosurgical Society
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• v.46 no.4
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• pp.365-369
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• 2009

Objective : Susceptibility-weighted image (SWI) is a sensitive magnetic resonance image (MRI) technique to detect cerebral microbleeds (MBLs). which would not be detected by conventional MRI. We performed SWI to detect MBLs and investigated its usefulness in the evaluation of mild traumatic brain injury (MTBI) patients. Methods : From December 2006 to June 2007, twenty-one MTBI patients without any parenchymal hemorrhage on conventional MRI were selected. Forty-two patients without trauma were selected for control group. According to the presence of MBLs, we divided the MTBI group into MBLs positive [SWI (+)] and negative [SWI (-)] group. Regional distribution of MBLs and clinical factors were compared between groups. Results : Fifty-one MBLs appeared in 16 patients of SWI (+) group and 16 MBLs in 10 patients of control group [control (+)], respectively. In SWI (+) group, MBLs were located more frequently in white matters than in deep nucleus different from the control (+) group (p<0.05). Nine patients (56.3%) of SW (+) group had various neurological deficits (disorientation in 4, visual field defect in 2, hearing difficulty in 2 and Parkinson syndrome in 1). Initial Glasgow Coma Scale (GCS)/mean Glasgow Outcome Scale (GOS) were $13.9{\pm}1.5/4.7{\pm}0.8$ and $15.0{\pm}0.0/5.0{\pm}0.0$ in SWI (+) and SWI (-) groups, respectively (p<0.05). Conclusion : Traumatic cerebral MBLs showed characteristic regional distribution, and seemed to have an importance on the initial neurological status and the prognosis. SWI is useful for detection of traumatic cerebral MBLs, and can provide etiologic evidences for some post-traumatic neurologic deficits which were unexplainable with conventional MRI.

• Investigative Magnetic Resonance Imaging
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• v.12 no.2
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• pp.142-147
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• 2008

Purpose : By using the micro-imaging unit modified from NMR spectrometer, the high resolution MRI protocols of finer than 100 micron in 5 minutes, is sought for mouse, which plays a central role in animal studies Materials and Methods : C57BL/6 mouse, lighter than 50 gram, is used for the experiments. The superconducting magnet is vertical type with 89 mm inner diameter at 4.9 Tesla. The diameter of rf-coil is 30 mm. Mostly used techniques are the fast spin echo and the gradient echo pulse sequence. Results : For 2D images, proton density and T2 weighted images are obtained and their optimum experimental variables were sought. Minute structure of mouse brain can be recognized and 3D brain image is also obtained additionally. 3D image will be useful particularly for the dynamic contrast study using various contrast agents. Conclusion : Like the case of human and other small animals, the high resolution of mouse brain is enough to recognize the minute structure of it. Recently, similar studies are reported domestically, but it seems only a beginning stage. Due to easiness of breeding/control, mouse MRI study will soon play a vital part in brain study.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.9-14
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• 2021

Recently, magnetic resonance imaging (MRI), which can acquire images with good contrast without exposure to radiation, has been widely used for diagnosis. However, noise that reduces the accuracy of diagnosis is essentially generated when acquiring the MR images, and by adjusting the parameters, the noise problem can be solved to obtain an image with excellent characteristics. Among the parameters, the number of excitation (NEX) can acquire images with excellent characteristics without additional degradation of image characteristics. In contrast, appropriate NEX setting is required since the scan time increases and motion artifacts may occur. Therefore, in this study, after fixing all MRI parameters through the MRiLab simulation program, we tried to evaluate the tendency of image characteristics according to changing NEX through quantitative evaluation of brain T2 weighted images acquired by adjusting only NEX. To evaluate the noise level and similarity of the acquired image, signal to noise ratio (SNR), contrast to noise ratio (CNR), root mean square error (RMSE) and peak signal to noise ratio (PSNR) were calculated. As a result, both noise level and similarity evaluation factors showed improved values as NEX increased, while the increasing width gradually decreased. In conclusion, we demonstrated that an appropriate NEX setting is important because an excessively large NEX does not affect image characteristics improvement and cause motion artifacts due to a long scan.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.1-5
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• 2003

의료영상의 분할은 의료영상을 컴퓨터 진단 및 가시화에 필요한 같은 성질을 가진 여러 조직으로 나누어주는 방법이다. 즉 입력되어진 영상을 처리하여 유사한 화소들의 집합인 영역들로 화소들을 구분하는 작업이며 영상분할의 결과는 영상인식의 정확성에 큰 영향을 미친다. MRI(Magnetic Resonance Imaging)으로부터 정상적인 세포조직 또는 뇌종양과 같은 비정상적인 세포조직의 가시화와 분석을 위해서는 대상 세포조직의 적절한 분류를 필요로 한다. 하지만 기존의 영역 검출 방법으로는 잡음이 섞여 있는 영상에서 여러 가지의 처리과정(주로 잡음 제거)이 필수적이고 그런 과정으로 인해 정확한 영역 검출이 힘들게 된다. 이에 잡음이 있더라도 이를 제거하기 위한 처리가 필요 없이 영역기반으로 필요한 파라미터의 추정을 통한 MRF(Markov Random Field)를 이용하여 보다 효율적이고 정확하게 MRI에서 질환 영역을 검출할 수 있다.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.397-403
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• 2023

The purpose of this study is to obtain brain MRI images through Heavenly T₂ FLAIR and DWI techniques to find out strengths and weaknesses of each image. Data were analyzed on 13 normal people and 17 brain tumor patients. Philips Ingenia 3.0TCX was used as the equipment used for the inspection, and 32 Channel Head Coil was used to acquire data. Using Image J and Infinity PACS Data, 3mm² of gray matter, white matter, cerebellum, basal ganglia, and tumor areas were set and measured. Quantitative analysis measured SNR and CNR as an analysis method, and qualitative analysis evaluated overall image quality, lesion conspicuity, image distortion, susceptibility artifact and ghost artifact on a 5-point scale. The statistical significance of data analysis was that Wilcox-on Signed Rank Test and Paired t-test were executed, and the statistical program used was SPSS ver.22.0 and the p value was less than 0.05. In quantitative analysis, the SNR of gray matter, white matter, cerebellum, basal ganglia, and tumor of Heavily T₂ FLAIR is 41.45±0.13, 40.52±0.45, 41.44±0.51, 40.96±0.09, 35.28±0.46 and the CNR is 15.24±0.13, 16.75±0.23, 16.28±0.41, 15.83±0.17, 16.63±0.51. In DWI, SNR is 32.58±0.22, 36.75±0.17, 30.21±0.19, 35.83±0.11, 43.29±0.08, and CNR is 13.14±0.63, 14.21±0.31, 12.95±0.32, 11.73±0.09, 17.56±0.52. In normal tissues, Heavenly T₂ FLAIR obtained high results, but in disease evaluation, high results were obtained at DWI, b=1000 (p<0.05). In addition, in the qualitative analysis, overall image quality, lesion conspicuity, image distortion, susceptibility artifact and ghost artifact aspects of the Heavily T₂ FLAIR were evaluated, and 3.75±0.28, 2.29±0.24, 3.86±0.23, 4.08±0.21, 3.79±0.22 values were found, respectively, and 2.53±0.39, 4.13±0.29, 1.90±0.20, 1.81±0.21, 1.52±0.45 in DWI. As a result of qualitative analysis, overall image quality, image distortion, susceptibility artifact and ghost artifact were rated higher than DWI. However, DWI was evaluated higher in lesion conspicuity (p<0.05). In normal tissues, the level of Heavenly T₂ FLAIR was higher, but the DWI technique was higher in the evaluation of the disease (tumor). The two results were necessary techniques depending on the normal site and the location of the disease. In conclusion, statistically significant results were obtained from the two techniques. In quantitative and qualitative analysis, the two techniques had advantages and disadvantages, and in normal and disease evaluation, the two techniques produced useful results. These results are believed to be educational data for clinical basic evaluation and MRI in the future.