• Progress in Medical Physics
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• v.15 no.2
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• pp.112-119
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• 2004

We tried to build human models based on medical images of live Korean, instead of using standard data of human body structures. Characteristics of mechanical structures of human bodies were obtained from medical images such as CT and MR images. For each constitutional part of mechanical structures CT images were analyzed in terms of gray levels and MR images were analyzed in terms of pulse sequence. Characteristic features of various mechanical structures were extracted from the analyses. Based on the characteristics of each structuring element we peformed image segmentation on CT and MR images. We delineated bones, muscles, ligaments and tendons from CT and MR images using image segmentation or manual drawing. For the image segmentation we compared the edge detection method, region growing method and intensity threshold method and applied an optimal compound of these methods for the best segmentation results. Segmented mechanical structures of the head/neck part were three dimensionally reconstructed.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.33-41
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• 2004

Accuracy of registration between images acquired from various medical image modalities is one of the critical issues in radiation treatment planing. In this study, a method of accuracy evaluation of image registration using a homemade brain phantom was investigated. Chamfer matching of CT-MR and CT-SPECT imaging was applied for the multimodal image registration. The accuracy of image correlation was evaluated by comparing the center points of the inserted targets of the phantom. The three dimensional root-mean-square translation deviations of the CT-MR and CT-SPECT registration were 2.1${\pm}$0.8 mm and 2.8${\pm}$1.4 mm, respectively. The rotational errors were ＜ 2$^{\circ}$ for the three orthogonal axes. These errors were within a reasonable margin compared with the previous phantom studies. A visual inspection of the superimposed CT-MR and CT- SPECT images also showed good matching results.

• Korean Journal of Metals and Materials
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• v.50 no.5
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• pp.401-406
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• 2012

When viewed using a magnetic resonance imaging (MRI) system, invasive materials inside the human body, in many cases, severely distort the MR image of human tissues. The degree of the MR image distortion increases in proportion not only to the difference in the susceptibility between the invasive material and the human tissue, but also to the intensity of the magnetic field induced by the MRI system. In this study, by blending paramagnetic Ti particles with diamagnetic graphite, we synthesized $Ti_{100-x}C_x$ composites that can reduce the artifact in the MR image under the high-strength magnetic field. Of the developed composites, $Ti_{70}C_{30}$ showed the magnetic susceptibility of ${\chi}=67.6{\times}10^{-6}$, which corresponds to 30% of those of commercially available Ti alloys, the lowest reported in the literature. The level of the MR image distortion in the vicinity of the $Ti_{70}C_{30}$ composite insert was nearly negligible even under the high magnetic field of 4.7 T. In this paper, we reported on a methodology of designing new structural materials for bio-applications, their synthesis, experimental confirmation and measurement of MR images.

• Korean Journal of Veterinary Research
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• v.44 no.2
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• pp.311-315
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• 2004

The detection of lymph node metastasis is an important step in tumor staging and is significant for therapy planning. It has been challenged to yield an appropriate image with diagnostic methods such as Magnetic Resonance (MR) and Computed Tomography (CT). Though CT has been used widely and accessed easily to show internal organs, it can hardly provide difference between lymph node and adjacent vessel or fat tissue. It has been well established that MR can reveal the subtle discrepancy within soft tissue. This study investigated the suitability of MR lymph node imaging without contrast enhancement by comparison of T1-weighted image (T1WI) and T2- weighted image (T2WI) in ten normal rabbits. According to the pulse sequence optimized from preliminary study, T1-weighted spin-echo (repetition time/echo time=400/12 ms) and T-2 weighted fast spin-echo (repetition time/echo time=3500/84 ms) images covering the hind limbs and pelvic region were acquired at 1.5 T. Two radiologists scrupulously evaluated the MR images in consensus. And signal intensity of lymph nodes was compared with that of adjacent fat. Statistical analysis showed that T1-weighted coronal image visualized the lymph nodes (iliac, superficial inguinal and popliteal lymph nodes) quickly and consistently rather than T2-weighted one. Conclusively, T1WI for evaluation of lymph nodes is moderately better than T2WI and appears to have potential for quick and sufficient mapping of the lymph nodes. In addition, this normal MR image of lymph nodes could be applied to further study for the evaluation of lymphatic system in abscess and tumor bearing animal model.

• 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 the Korean Society of Radiology
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• v.16 no.7
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• pp.835-842
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• 2022

This study is aimed to evaluate the effects of a synthetic MR technique in reducing metal artifacts. In the experiment, the in-plane and through-plane images were acquired by applying a synthetic MR technique and a high-speed spin echo technique to a phantom manufactured with screw for spinal surgery. The area of the metal artifact was compared. The metal artifacts were measured by dividing the signal-loss and the signal pile-up areas, and the area of the final artifact was calculated through the sum of the two. As a result, the metal artifacts were relatively reduced when the synthetic MR techniques were applied to both in-plane and through-plane. Comparing by sequence, the in-plane T1 images decreased by 23.45%, T2 images by 20.85%, PD images by 19.67%, and FLAIR images by 22.12%. Also, in the case of the through-plane, the T1 image decreased by 62.95%, the T2 image decreased by 73.93%, the PD image decreased by 74.68%, and the FLAIR image decreased by 66.43%. The cause of this result is that when the synthetic MR technique is applied, the distortion is due to the signal pile-up and does not occur and the size of the entire metal artifact is reduced. Therefore, synthetic MR technique can very effectively reduce metal artifacts, which can help to increase the diagnostic value of images.

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.1-15
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• 2002

A noel automated brain region extraction method in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in clue fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded Drain masks. This method can automatically extract a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.309-315
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• 2009

Image reconstruction of Inverse Fourier Transform after Frequency Domain Data is filtered applies to Image signal acquired from MR. There are various kinds of image processing techniques; image preprocessing, image reconstruction, image compression, image restoration image mixture, noise and artifact elimination, and image quality improvement. In this paper, optimum filter applicable to diagnosis in clinic by comparing and analyzing the characteristics of the filter will be explained. Fermi-Dirac filter will improve the image quality better than the previous MR image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2425-2431
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• 1997

The increased usage of Magnetic Resonance Image (MRI) required the method for automatic segmentation of medical image that is more useful so as to diagnose the dissecitive information of a atient quickly and effectively through MR scans.The use of neural networks may give much hep to solving the complex problems concerned the matter. This paper proposes the new method for automatic segmentation of magnetic resonance (MR) images of the brain by using neural networks brained by back-propagation algorithm. The trained neural networks by the segmenting MR images of a patient produce an output that networks can segment MR images of the other patients automatically, too and show a clear image of the brain.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.36-42
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• 1997

This paper presents a new design technique for performing high quality low bit rate image compression. A hybrid coder(HC) which combines Mean Removed Important Coefficient Selection based JPEG(MR-ICS-JPEG) and Adaptive Vector Quantization (AVQ) is proposed. A new quantization table is developed using the Important Coefficient Selection(ICS) method; the importance of each coefficient is determined using the orthonormal property of the DCT. This quantization table is applied to standard JPEG with mean removal(MR) strategy before processing. This scheme, called MR-ICS-JPEG, produces more than 2 dB enhanced performance in terms of PSNR over standard JPEG. A set of homogeneous codebooks is generated by homogeneous training vectors. Before compression, an image is uniformly divided into 8${\times}$8 blocks. Low detail regions such as backgrounds are roughly coded by AVQ while high detail regions such as edges or curves are finely coded by the proposed MR-ICS-JPEG. This hybrid coder procuces consistently about 3 dB improved performance in terms of PSNR over standard JPEG.