• Journal of Digital Convergence
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• v.12 no.2
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• pp.439-446
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• 2014

To evaluate the effects of an artifact by metal material for orthodontics in Magnetic Resonance Image (MRI) examination, wires and brackets used in orthodontics were selected and compared. Using a head coil, a $T_2$-weighted image, $T_1$-weighted image and FLAIR image were obtained. With obtained images, the sizes of the artifacts were measured and compared using Image J Program. In the research, the material with the biggest artifact in the wires and brackets for orthodontics was stainless steel wire. In the future, selecting and developing metal for correction should be considered also in other fields along with the purpose of orthodontics.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.124-135
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• 2012

Purpose : The objective of this study was to investigate the differences of hippocampal volume and shape as well as the functional change between long life experienced taxi drivers and controls of Korean population. Materials and Methods: Three-dimensional T1-weighted images and blood oxygen level dependent functional MRI(fMRI) were obtained from 8 subjects, consisting of 4 experienced (20-30 years) taxi drivers and 4 age-matched controls. The hippocampal volume and shape were analyzed with three-dimensional T1-weighted images. In addition, neuronal activities of brain were analyzed using a blood oxygen level dependent fMRI between the two groups. Results: The hippocampal volume showed no statistically significant difference between the two groups (p > 0.05). The left hippocampi of the taxi drivers were slightly elongated with larger head and tail portions than those of the controls (p < 0.05, uncorrected). For the functional MRI, fusiform gyrus was specifically activated in taxi drivers, compared with the control group. Conclusion: The structural and functional changes of taxi driver's hippocampus indicate the functional differentiation as a result of occupational dependence on spatial navigation. In other words, the continuous usage of spatial navigation performance may diminish degeneration of hippocampus and the related brain regions.

• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.42-49
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• 2004

Purpose : The quantization noise in magnetic resonance imaging (MRI) systems is analyzed. The signal-to-quantization noise ratio (SQNR) in the reconstructed image is derived from the level of quantization in the signal in spatial frequency domain. Based on the derived formula, the SQNRs in various main magnetic fields with different receiver systems are evaluated. From the evaluation, the quantization noise could be a major noise source determining overall system signal-to-noise ratio (SNR) in high field MRI system. A few methods to reduce the quantization noise are suggested. Materials and methods : In Fourier imaging methods, spin density distribution is encoded by phase and frequency encoding gradients in such a way that it becomes a distribution in the spatial frequency domain. Thus the quantization noise in the spatial frequency domain is expressed in terms of the SQNR in the reconstructed image. The validity of the derived formula is confirmed by experiments and computer simulation. Results : Using the derived formula, the SQNRs in various main magnetic fields with various receiver systems are evaluated. Since the quantization noise is proportional to the signal amplitude, yet it cannot be reduced by simple signal averaging, it could be a serious problem in high field imaging. In many receiver systems employing analog-to-digital converters (ADC) of 16 bits/sample, the quantization noise could be a major noise source limiting overall system SNR, especially in a high field imaging. Conclusion : The field strength of MRI system keeps going higher for functional imaging and spectroscopy. In high field MRI system, signal amplitude becomes larger with more susceptibility effect and wider spectral separation. Since the quantization noise is proportional to the signal amplitude, if the conversion bits of the ADCs in the receiver system are not large enough, the increase of signal amplitude may not be fully utilized for the SNR enhancement due to the increase of the quantization noise. Evaluation of the SQNR for various systems using the formula shows that the quantization noise could be a major noise source limiting overall system SNR, especially in three dimensional imaging in a high field imaging. Oversampling and off-center sampling would be an alternative solution to reduce the quantization noise without replacement of the receiver system.

• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.128-128
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• 2002

목적： Gamma-Variate Function을 이용하여 뇌 관류에 관련하는 새로운 재구성 영상 (CBV, CBF, MTT, TTP, BAT)을 산출할 수 있는 프로그램을 개발 및 평가 대상 및 방법： 뇌 관류와 관련한 정량적인 재구성 영상(CBV, CBF, MTT, TTP, BAT)을 얻기 위하여 Gamma-Variate Function을 이용한 Post-processing 프로그램을 개발하였다. 자기공명영상은 1.5T ＆ 3.0T Magnum (Medinus Co., Ltd.)을 사용하였으면, 개발된 프로그램 평가를 위해 스핀에코 -EPI 펄스시퀀스(TR 2000 ms, TE 80 ms, FOV 260 mm, matrix 92$\times$128)를 사용하여 관류강조영상을 획득하였다.

• 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.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Investigative Magnetic Resonance Imaging
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• v.3 no.1
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• pp.53-59
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• 1999

Purpose : To evaluate the usefulness of functional MR imaging (fMRI) for language mapping and determination of language lateralization. Materials and Methods : Functional maps of the language area were obtained during word generation tasks and decision task in ten volunteers (7 right handed, 3 left-handed). MR examinations were performed at 1.5T scanner with EPI BOLD technique. Each task consisted of three resting periods and two activation periods with each period of 30 seconds. Total acquisition time was 162 sec. SPM program was used for the postprocessing of images. Statistical comparisons were performed by using t-statistics on a pixel-by- pixel basis after global normalization by ANCOVA. Activation areas were topographically analyzed (p>0.001) and activated pixels in each hemisphere were compared quantitatively by lateralization index. Results : Significant activation signals were demonstrated in 9 of 10 volunteers. Activation signals were found in the premotor and motor cortices, the inferior frontal, inferior parietal, and mid-temporal lobes during stimulation tasks. In the right handed seven volunteers, activation of language areas was lateralized to the left side. Verb generation task produced stronger activation in the language areas and higher value of lateralization index than noun generation task or decision task. Conclusion : fMRI could be a useful non-invasive method for language mapping and determination of language dominance.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free 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 he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• 대한족부족관절학회:학술대회논문집
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• 2008.11a
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• pp.45-46
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• 2008

• The Academic Congress of Korean Shoulder and Elbow Society
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• 2009.03a
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• pp.90-90
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• 2009