• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.940-952
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• 2020

The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

• The Journal of the Korea Contents Association
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• v.3 no.3
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• pp.85-97
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• 2003

Medical tomography images like CT, MRI, PET, SPECT, fMRI, ett have been widely used for diagnosis and treatment of a patient and for clinical study in hospital. In many cases, tomography images are scanned in several different modalities or with time intervals for a single subject for extracting complementary information and comparing one another. 3D image registration is mapping two sets of images for comparison onto common 3D coordinate space, and may be categorized to marker -based matching and feature-based matching. 3D registration of brain images has an important role for visual and quantitative analysis in localization of treatment area of a brain, brain functional research, brain mapping research, and so on. In this article, marker-based and feature-based matching methods which are often used are introduced.

• Smart Media Journal
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• v.5 no.1
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• pp.44-48
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• 2016

In a longitudinal neuroimaging study, the upgrades of a magnetic resonance imaging (MRI) scanner due to outdated hardwares and softwares make it difficult to maintain the same MRI conditions in the long-term research period. Particularly, high field MRI systems such 3T scanners become popular in recent years. However, it is still unclear whether an integrated analysis of 3T and 1.5T images is possible without consideration of the field strength. In this study, we evaluated the reproducibility of hippocampal volumes between brain images with different field strengths and slice orientations. 296 participants underwent both 3T and 1.5T MRI and both sagittal and axial scans for high resolution brain images, and their hippocampal volumes were measured using Freesurfer, a well-known software for neuroimaging analysis. Paired t-tests showed that the hippocampal volumes were significantly different between the image types. These results suggest that it is necessary to develop data analysis techniques for integrating diverse types of MRI images.

• Science of Emotion and Sensibility
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• v.14 no.3
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• pp.403-414
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• 2011

The current study examined brain regions associated with aesthetic experience to fractal images using functional MRI. The aesthetic estimations of the images showed that there is a general consensus regarding the perception of beautiful images. Out of 270 fractal images, fifty images rated highest(beautiful images) and fifty images rated lowest(non-beautiful images) were selected and presented to the participants. The two conditions were presented using the block design. Frontal lobes, cingulate gyri, and insula, the areas related to the cognitive and emotional processing in aesthetic experience, were activated when beautiful images were presented. In contrast, the middle occipital gyri and precuneus, the areas associated with experience of negative emotions, were activated when non-beautiful images were presented. The conjunction analysis showed activations in temporal areas in response to beautiful images and activations in parietal areas in response to non-beautiful images. These results indicate that beautiful images elicit semantic interpretations whereas non-beautiful images facilitate abstract processes.

• Investigative Magnetic Resonance Imaging
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• v.13 no.2
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• pp.183-189
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• 2009

Purpose : To evaluate the usefulness of three-dimensional (3D) maximal intensity projection (MIP) reconstruction method in breast MRI. Materials and Methods : Total 54 breasts of consecutive 27 patients were examined by breast MRI. Breast MRI was performed using GE Signa Excite Twin speed (GE medical system, Wisconsin, USA) 1.5T. We obtained routine breast MR images including axial T2WI, T1WI, sagittal T1FS, dynamic contrast-enhanced T1FS, and subtraction images. 3D MIP reconstruction images were obtained as follows; subtraction images were obtained using TIPS and early stage of contrast-enhanced TIPS images. And then 3D MIP images were obtained using the subtraction images through advantage workstation (GE Medical system). We detected and analyzed the lesions in the 3D MIP and routine MRI images according to ACR $BIRADS^{(R)}$ MRI lexicon. And then we compared the findings of 3D MIP and those of routine breast MR images and evaluated whether 3D MIP had additional information comparing to routine MR images. Results : 3D MIP images detect the 43 of 56 masses found on routine MR images (76.8%). In non-mass like enhancement, 3D MIP detected 17 of 20 lesions (85 %). And there were one hundred sixty nine foci at 3D MIP images and one hundred nine foci at routine MR images. 3D MIP images detected 14 of 23 category 3 lesions (60.9%), 11 of 16 category 4 lesions (68.87%), 28 of 28 Category 5 lesions (100%). In analyzing the enhancing lesions at 3D MIP images, assessment categories of the lesions were correlated as the results at routine MR images (p-value < 0.0001). 3D MIP detected additional two daughter nodules that were descriped foci at routine MR images and additional one nodule that was not detected at routine MR images. Conclusion : 3D MIP image has some limitations but is useful as additional image of routine breast MR Images.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.157-164
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• 2011

The purpose of this study was to examine the usefulness of 3D reconstruction images in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, subtraction images and 3D reconstruction images were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: The 3D reconstruction images showed higher SNR at the lesion area, ductal area, and fat area that of the subtraction image. In addition, the CNR were not significantly different in the lesion area itself between the subtraction images and 3D reconstruction images.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.109-109
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• 2001

Purpose： To assess the feasibility of sequential administration of ferumoxides and mangafodi trisodium in the same imaging protocols. Method： Thirty patients underwent double-contrast enhanced MR imaging of liver usi ferumoxides (Fe-MRI) and mangafodipir trisodium (Mn-MRI) on 1.5T GE Horizon system. In twenty patients, Mn-MRI was immediately followed by Fe-MRI. In ten patients, Fe-MR was performed first, then Mn-MRI was performed immediately, In all cases, precontras T1-weighted in-phase and opposed-phase spoiled gradient echo (GRE) images an T2-weighted fast spin-echo images (TR 4000ms, TE 102ms, ETL 8-12) were obtained Fe-MRI was performed with FSE and steady state GRE (TE 10 msec, flip angle 30 sequences. Mn-MRI was performed with in-phase and opposed-phase spoiled GR sequences. The SNR changes after the use of each contrast agents were calculated.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.69-78
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• 2016

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.