• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.7-10
• /
• 2007

많은 과학자들은 인간의 사고를 functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG)등을 이용해서 두뇌 활동 영역을 연구하고 있다. 주로 의학 분야와 심리학의 영역에서 두뇌의 활동을 연구하여 간질이나 발작을 알아내고 거짓말 탐지 분야에서도 사용된다. 본 논문에서는 사람의 두뇌활동을 측정하여 인간의 감정을 인식하는 연구에 중점을 두었다. 특히 fMRI와 TRS 그리고 EEG를 이용해서 사람의 두뇌활동을 측정하는 연구를 하였다. 많은 연구자들이 한 가지 측정 장치만을 사용하여서 측정하거나 fMRI와 EEG를 동시에 측정하는 연구를 진행하고 있다. 현재에는 단순히 두뇌의 활동을 측정하거나 측정시 발생하는 잡음들을 제거하는 연구들에 중점을 두고 진행되고 있다. 본 연구에서는 fMRI와 TRS를 동시에 측정하여 얻은 두뇌 활동 데이터를 가지고 감정에 따른 활동영역의 EEG신호를 측정하였다. EEG 신호분석에 있어서 기존의 뇌파만을 가지고 특정을 찾아내는 것을 넘어서 각각의 채널에서 기록되는 뇌파의 파형을 주파수에 따라서 분류하고 정확한 측정을 위해 낮은 주파수를 제거하고 연구자가 필요한 부분의 뇌파를 분석하였다.

• Korean Journal of Radiology
• /
• v.24 no.6
• /
• pp.553-563
• /
• 2023

Objective: Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging-derived tractography (DTI-t) contribute to the localization of language areas, but their accuracy remains controversial. This study aimed to investigate the diagnostic performance of preoperative fMRI and DTI-t obtained with a simultaneous multi-slice technique using intraoperative direct cortical stimulation (DCS) or corticocortical evoked potential (CCEP) as reference standards. Materials and Methods: This prospective study included 26 patients (23-74 years; male:female, 13:13) with tumors in the vicinity of Broca's area who underwent preoperative fMRI and DTI-t. A site-by-site comparison between preoperative (fMRI and DTI-t) and intraoperative language mapping (DCS or CCEP) was performed for 226 cortical sites to calculate the sensitivity and specificity of fMRI and DTI-t for mapping Broca's areas. For sites with positive signals on fMRI or DTI-t, the true-positive rate (TPR) was calculated based on the concordance and discordance between fMRI and DTI-t. Results: Among 226 cortical sites, DCS was performed in 100 sites and CCEP was performed in 166 sites. The specificities of fMRI and DTI-t ranged from 72.4% (63/87) to 96.8% (122/126), respectively. The sensitivities of fMRI (except for verb generation) and DTI-t were 69.2% (9/13) to 92.3% (12/13) with DCS as the reference standard, and 40.0% (16/40) or lower with CCEP as the reference standard. For sites with preoperative fMRI or DTI-t positivity (n = 82), the TPR was high when fMRI and DTI-t were concordant (81.2% and 100% using DCS and CCEP, respectively, as the reference standards) and low when fMRI and DTI-t were discordant (≤ 24.2%). Conclusion: fMRI and DTI-t are sensitive and specific for mapping Broca's area compared with DCS and specific but insensitive compared with CCEP. A site with a positive signal on both fMRI and DTI-t represents a high probability of being an essential language area.

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

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

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.550-554
• /
• 1997

Acoustic or sound noise due to gradient pulsing has been one of the problems in MRI, both in patient scanning as well as in many areas of psychiatric and neuroscience research, such as unctional MRI (fMRI). Our recent observations in MRI or the visual and motor cortex show very different results with sound noise in comparison with the results obtained without sound noise. Although a number of ideas has been suggested in the literature about the possible elimination or reduction of sound noise, progress has been slow due to the basic role of gradient pulsing in MR imaging. Therefore, we report on some typical behavior of sound noise observed from MRI scanners and the analyses of their characteristics. Data are obtained both from a commercial MRI scanner (GE Signa 1.5-T EPI system).

• Investigative Magnetic Resonance Imaging
• /
• v.23 no.3
• /
• pp.220-227
• /
• 2019

Purpose: To evaluate the usefulness of wash-in color map in detecting early enhancement of prostate focal lesion compared to whole dynamic contrast-enhanced MRI (DEC MRI) images. Materials and Methods: This study engaged 50 prostate cancer patients who underwent multiparametric MRI and radical prostatectomy as subjects. An expert [R1] and a trainee [R2] independently evaluated early enhancement and recorded the time needed to review 1) a wash-in color map and 2) whole DCE MRI images. Results: The review of whole DCE images by R1 showed fair agreement with color map by R1, whole images by R2, and color map by R2 (weighted kappa values = 0.59, 0.44, and 0.58, respectively). Both readers took a significantly shorter time to review the color maps as compared to whole images (P < 0.001). Conclusion: A trainee could achieve better agreement with an expert when using wash-in color maps than when using whole DCE MRI images. Also, color maps took a significantly shorter evaluation time than whole images.

• Investigative Magnetic Resonance Imaging
• /
• v.14 no.1
• /
• pp.1-9
• /
• 2010

Although ultrasonography is the most commonly used first-line imaging modality of the female pelvis because of diagnostic accuracy, low cost and safety, MRI is the best imaging modality of choice for the evaluation of the female pelvis. The indication of female pelvis MRI is diverse and includes the evaluation of M$\ddot{u}$llerian duct anomaly, differential diagnosis and characterization of uterine and ovarian tumors, and staging of malignant uterine and ovarian tumors. Understanding of MR protocols according to the specific gynecologic pathology allows accurate diagnosis and proper patient management.

• Investigative Magnetic Resonance Imaging
• /
• v.22 no.3
• /
• pp.194-199
• /
• 2018

We present image findings, especially rare MRI of a primary breast angiosarcoma with its histopathology, and also analyze the relevant medical literature reports in terms of the MRI findings. As our patient had unique features of a primary breast angiosarcoma, this case could be very helpful for future diagnosis of this rare breast malignancy by MRI.

• Proceedings of the KSMRM Conference
• /
• 1999.11a
• /
• pp.55-56
• /
• 1999

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.233-242
• /
• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.