• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.700-707
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• 2013

In this paper, we propose a non-rigid registration method of lung parenchyma in temporal chest CT scans using region binarization modeling and locally deformable model. To cope with intensity differences between CT scans, we segment the lung vessel and parenchyma in each scan and perform binarization modeling. Then, we match them without referring any intensity information. We globally align two lung surfaces. Then, locally deformable transformation model is developed for the subsequent non-rigid registration. Subtracted quantification results after non-rigid registration are visualized by pre-defined color map. Experimental results showed that proposed registration method correctly aligned lung parenchyma in the full inspiration and expiration CT images for ten patients. Our non-rigid lung registration method may be useful for the assessment of various lung diseases by providing intuitive color-coded information of quantification results about lung parenchyma.

• Journal of the Korean Society of Radiology
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• v.13 no.7
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• pp.961-968
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• 2019

The purpose of this study was to evaluate the usefulness of cerebral angiography in each energy level by using dual energy technique in CT. Methods were performed on 15 DE images and SE images of CT angiography. For the analysis of images, mean value, standard deviation, SNR and CNR value were determined by setting ROI on MCA, brain parenchyma tissue, and back ground. As a result of concurrent visual evaluation with Likert 5 point scale, the clearest MCA image was confirmed at DE 40 keV and SE 120 kVp(p>0.05). The SNR value of the SE image was measured to be similar to the 40 keV energy level of the DE image. The low energy level image of 40 keV and 50 keV was measured with a high SNR and the contrast ratio was higher than that of the high energy image.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.270-296
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• 2024

Coronary CT angiography (CCTA) is recognized for its role as a gatekeeper for invasive coronary angiography in patients suspected of coronary artery disease because it can detect significant coronary stenosis with high accuracy. However, heavy plaque in the coronary artery makes it difficult to visualize the lumen, which can lead to errors in the interpretation of the CCTA results. This is primarily due to the limited spatial resolution of CT scanners, resulting in blooming artifacts caused by calcium. However, coronary stenosis with high calcium scores often requires evaluation using CCTA. Technological methods to overcome these limitations include the introduction of high-resolution CT scanners, the development of reconstruction techniques, and the subtraction technique. Methods to improve reading ability, such as the setting of appropriate window width and height, and evaluation of the position of calcified plaque and residual visibility of the lumen in cross-sectional images, are also recommended.

• Journal of the Korean Society of Radiology
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• v.85 no.1
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• pp.24-35
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• 2024

Spontaneous intracranial hypotension (SIH), which generally presents as orthostatic headache, is increasingly being identified due to improved imaging technologies and heightened awareness. Many prior studies have reported the characteristic brain MRI findings of SIH. However, recently, focus has shifted to spinal MRI, as SIH is believed to be caused by leakage of cerebrospinal fluid from the spinal dural sac. Advanced techniques such as ultrafast CT myelography and digital subtraction myelography have emerged as useful technique to identify the site of cerebrospinal fluid leakage. In this review, we discuss the diagnosis, spinal MRI findings, imaging techniques, and treatment of SIH.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.