• Speech Sciences
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• v.2
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• pp.45-55
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• 1997

This paper describes the procedure of implementing an articulatory speech simulator, in order to model the human articulatory organs and to synthesize speech from this model after. Images required to construct the vocal tract model were obtained from MRI, they were then used to construct 2D and 3D vocal tract shapes. In this paper 3D vocal tract shapes were constructed by spatially concatenating and interpolating sectional MRI images. 2D vocal tract shapes were constructed and analyzed automatically into a digital filter model. Following this speech sounds corresponding to the model were then synthesized from the filter. All procedures in this study were using MATLAB.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.429-432
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• 2015

Radio Frequency (RF) coils in Magnetic Resonance Imaging (MRI) systems interact with a patient's tissues, resulting in the absorption of RF energy by the tissues. The presence of an electrically conducting medical implant may concentrate the RF energy and causes tissue heating near the implant devices. Here we present a novel design for a medical lead to reduce this undesired heating. Specific Absorption Rate (SAR), an indicator of heating, was calculated. Remcom XFdtd software was used to calculate the peak SAR distribution (1g and 10 g) in a realistic model of the human body. The model contained a medical lead that was exposed to RF magnetic fields at 64 MHz (1.5 T MRI), 128 MHz (3 T MRI) and 300 MHz (7 T MRI) using a model of an MR birdcage body coil. Our results demonstrate that, our proposed design of adding nails to the medical lead can significantly reduce the SAR for different MRI systems.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.566-572
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• 2014

The hippocampus has been known as one of the most important structure related to many neurological disorders, such as Alzheimer's disease. This paper presents the snake model to segment hippocampus from brain MRI. The snake model or active contour model is widely used in medical image processing fields, especially image segmentation they look onto nearby edge, localizing them accurately. We applied a snake model on brain MRI. Then we compared our results with an active shape approach. The results show that hippocampus was successfully segmented by the snake model.

• Smart Media Journal
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• v.9 no.2
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.626-639
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• 2023

Objective: To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC). Materials and Methods: This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them. Results: Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set. Conclusion: The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1129-1145
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• 2016

Functional neuro-connectivity is one of the main issues in brain science in the sense that it is closely related to neurodynamics in the brain. In the paper, we choose fMRI as a main form of response data to brain activity due to its high resolution. We review methods for analyzing functional neuro-connectivity assuming that measurements are made on physiological responses to neuron activation. This means that we deal with a state-space and measurement model, where the state-space model is assumed to represent neurodynamics. Analysis methods and their interpretation should vary subject to what was measured. We included analysis results of real fMRI data by applying a high-dimensional autoregressive model, which indicated that different neurodynamics were required for solving different types of geometric problems.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.40-48
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• 2024

This study presents an in-vitro model designed to simulate mitral valve regurgitation, aiming to compare the quantification results between Proximal Isovelocity Surface Area(PISA) and 4D Flow MRI on both fixed and valve annulus tracking(VAT) views. The in-vitro model replicates the dynamic conditions of the mitral valve in a pulsatile environment, utilizing a piston pump set at 60 bpm. Through systematic experiments and analysis, the study evaluates the accuracy and effectiveness of PISA and 4D Flow MRI in assessing regurgitation severity, considering both fixed and valve annulus tracking. The displacement length measured in echo closely resembled that of optical measurements, making it advantageous for structural analysis. VAT-4D flow MRI exhibited the smallest deviation from actual flow rate values, establishing it as most accurate method for quantitative regurgitation assessment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.425-427
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• 2022

The grade of glioma is important information related to survival and thus is important to classify the grade of glioma before treatment to evaluate tumor progression and treatment planning. Glioma grading is mostly divided into high-grade glioma (HGG) and low-grade glioma (LGG). In this study, image preprocessing techniques are applied to analyze magnetic resonance imaging (MRI) using the deep neural network model. Classification performance of the deep neural network model is evaluated. The highest-performance EfficientNet-B6 model shows results of accuracy 0.9046, sensitivity 0.9570, specificity 0.7976, AUC 0.8702, and F1-Score 0.8152 in 5-fold cross-validation.