• Journal of the Korean Society of Radiology
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• v.16 no.2
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• pp.103-113
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• 2022

In this paper, a combined ¹⁸F-FDG(fluorodeoxyglucose) and MNP(magnetic nanoparticles) contrast agent was synthesized using N-(p-maleimidophenyl) isocyanate as the crosslinker for use in simultaneous PET-MRI scans. PET-MRI images were acquired and evaluated before and after injection of the combined contrast imaging agent (¹⁸F-FDG labeled MNP) from a glioma stem cell mouse model. After setting the region of interest (ROI) on each acquired image, the area of the lesion was calculated by segmentation. As a result, the PET image was larger than the MRI. In particular, the simultaneous PET-MRI images showed accurate lesions along with the surrounding soft tissue. The mean and standard deviation values were higher in the MRI images alone than in the PET images or the simultaneous PET-MRI images, regardless of whether the contrast agent was injected. In addition, the simultaneous PET-MRI image values were higher than for the PET images. For PSNR experiments, the original image was PET Image using 18F-FDG, MRI using MNPs, and MRI without contrast medium, and the target image was simultaneous PET-MRI image using 18F-FDG labeled MNPs contrast medium. As a result, all of them appeared significantly, suggesting that the 18F-FDG labeled MNPs contrast medium is useful. Future research is needed to develop an agent that can simultaneously diagnose and treat through SPECT-MRI imaging research that can use various nuclides.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.374-383
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• 2024

Objective: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learningbased image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE). Materials and Methods: This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols. Results: The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, P < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, P = 0.001), with improved depiction of hippocampal T2 high signal intensity change (P = 0.016) and loss of internal structure (P < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, P = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, P = 0.010), image quality, SNR, and CNR (all, P < 0.001). Conclusion: The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.

• Journal of the Korean Society of Radiology
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• v.3 no.4
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• pp.13-21
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• 2009

The Magnetic Resonance Imaging (MRI) systems consist of various parameters. Among them, the image quality can be arguably the most important part of the systems. As the other components in MRI systems have been developed and evolved, the MRI image quality has been advanced remarkably. And, the radiation imaging system is being converted from the Film to the digital method, which drives the computerization of many hospitals. The management of the tremendous radiation images becomes more critical. The data compression is used to store such large data in a network server. When the image files are compressed, the image quality degrades comparing to its original images. Even slight quality degradation of a medical image could cause an erroneous diagnosis, so the images must be handled carefully. This thesis studied the image assessment methods of comparing the quality of the compressed image to its original, and the quality of the original and the displayed images of the MRI systems via PSNR with actual medical images used in hospitals. As a result, no noticeable quality degradation was found comparing the compressed images with various digital compression methods and the original images. However, it was a different story comparing the original images and the displayed images on MRI monitors. Some noise or image distortion was visible using any regular CRT or LCD monitors were used while the special monitors designed for the MRI imaging and medical images displayed high definition images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.948-958
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• 2010

Human body signals collected by the MRI system are very weak, such that they may be easily affected by either external noise or system instability while being imaged. Therefore, this paper analyzes the nonuniformity caused by a design of the RF receiving coil in a low-magnetic-field MRI system, and proposes an efficient method to improve the image uniformity. In this paper, a method for acquiring 3D bias volume data by using phantom data among various methods for correcting such nonuniformity in MRI image is proposed, such that it is possible to correct various-sized images. It is shown by simulations that images obtained by various imaging methods can be effectively corrected using single bias data.

• Journal of radiological science and technology
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• v.44 no.1
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• pp.31-37
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• 2021

This study aim of quantitative assessment of Noise Power Spectrum(NPS) and image characteristics of by acquired the optimal image for noise characteristics and quality assurance by using magnetic resonance imaging(MRI). MRI device was (MAGNETOM Vida 3.0T MRI; Siemense healthcare system; Germany) used and the head/neck shim MR receive coil were 20 channels coil and a diameter 200 mm hemisphere phantom. Frequency signal could be acquired the K-space trajectory image and white image for NPS. The T2 image highest quantitatively value for NPS finding of showed the best value of 0.026 based on the T2 frequency of 1.0 mm-1. The NPS acquired of showed that the T1 CE turbo image was 0.077, the T1 CE Conca2 turbo image was 0.056, T1 turbo image was 0.061, and the T1 Conca2 turbo image was 0.066. The assessment of NPS image characteristics of this study were to that could be used efficiently of the MRI and to present the quantitative evaluation methods and image noise characteristics of 3.0T MRI.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.339-346
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• 2003

MRI provides anatomical structure information with superb spatial resolution that can be utilized in clinical surgeries. Advanced image processing techniques in conjunction with the MRI-guided surgery is expected to be of great importance in brain surgeries in the near future. In this paper, we introduce an image-guided surgery technique using the stereo matching method. To perform image-guided biopsy operations, we made MRI markers, camera markers and a detection probe marker. To evaluate the accuracy of the image-guided system. we made a silicone phantom. Using the phantom and markers, we have performed MRI-guided experiments with a 1.5 Tesla MRI system. It has been verified from phantom experiments that our system has a positioning error less than 1.5%. Compared with other image guided surgery system, our system shows better positioning accuracy.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.11-18
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• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.