• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.450-453
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• 2002

In radiotherapy treatment planning, it is critical to deliver the radiation dose to tumor and protect surrounding normal tissue. Recent developments in functional imaging and radiotherapy treatment technology have been raising chances to control tumor saving normal tissues. A brain phantom which could be used for image registration technique of CT-MR and CT-SPECT images using surface matching was developed. The brain phantom was specially designed to obtain imaging dataset of CT, MR, and SPECT. The phantom had an external frame with 4 N-shaped pipes filled with acryl rods, Pb rods for CT, MR, and SPECT imaging, respectively. 8 acrylic pipes were inserted into the empty space of the brain phantom to be imaged for geometric evaluation of the matching. For an optimization algorithm of image registration, we used Downhill simplex algorithm suggested as a fast surface matching algorithm. Accuracy of image fusion was assessed by the comparison between the center points of the section of N-shaped bars in the external frame and the inserted pipes of the phantom and minimized cost functions of the optimization algorithm. Technique with partially transparent, mixed images using color on gray was used for visual assessment of the image registration process. The errors of image registration of CT-MR and CT-SPECT were within 2mm and 4mm, respectively. Since these errors were considered within a reasonable margin from the phantom study, the phantom is expected to be used for conventional image registration between multimodal image datasets..

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• Korean Journal of Biological Psychiatry
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• v.18 no.2
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• pp.55-60
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• 2011

Neuroimaging studies in schizophrenia have remarkably increased and provided some clues to understand its pathophysiology. Here, we reviewed the neuroimaging, studies including volume analysis, functional magnetic resonance imaging (MRI) and diffusion tensor imaging, and findings in both early stage schizophrenia and high-risk group. The reviewed studies suggested that the brain with schizophrenia showed both regional deficits and dysconnectivity of neural circuit in the first episode, even high-risk group as well as chronic schizophrenia. Multimodal neuroimaging or combined approach with genetic, electro-or magneto-encephalographic data could provide promising results to understand schizophrenia in the near future.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.63-66
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• 2019

Due to the development of medical imaging technology, different imaging technologies provide a large amount of effective information. However, different imaging method caused the limitations of information integrity by using single type of image. Combining different image together so that doctor can obtain the information from medical image comprehensively. Image registration algorithm based on mutual information has become one of the hotspots in the field of image registration with its high registration accuracy and wide applicability. Because the information theory-based registration technology is not dependent on the gray value difference of the image, and it is very suitable for multimodal medical image registration. However, the method based on mutual information has a robustness problem. The essential reason is that the mutual information itself is not have enough information between the pixel pairs, so that the mutual information is unstable during the registration process. A large number of local extreme values are generated, which finally cause mismatch. In order to overcome the shortages of mutual information registration method, this paper proposes a registration method combined with image spatial structure information and mutual information.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.960-973
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• 2023

The bicuspid aortic valve (BAV) is the most common congenital cardiovascular malformation. Patients with BAV are at higher risk of other congenital cardiovascular malformations and valvular dysfunction, including aortic stenosis/regurgitation and infective endocarditis. BAV may also be related to aortic wall abnormalities such as aortic dilatation, aneurysm, and dissection. The morphology of the BAV varies with the presence and position of the raphe and is associated with the type of valvular dysfunction and aortopathy. Therefore, accurate diagnosis and effective treatment at an early stage are essential to prevent complications in patients with BAV. This pictorial essay highlights the characteristics of BAV and its related congenital cardiovascular malformations, valvular dysfunction, aortopathy, and other rare cardiac complications using multimodal imaging.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.

• Progress in Medical Physics
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• v.23 no.4
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• pp.309-316
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• 2012

Multimodal-imaging technique has been rapidly developed for improvement of diagnosis and evaluation of therapeutic effects. In despite of integrated hardware, registration accuracy was decreased due to a discrepancy between multimodal image and insufficiency of count in accordance with different acquisition method of each modality. The purpose of this study was to improve the PET image by event data resampling through analysis of data format, noise and statistical properties of small animal PET list data. Inveon PET listmode data was acquired as static data for 10 min after 60 min of 37 MBq/0.1 ml $^{18}F$-FDG injection via tail vein. Listmode data format was consist of packet containing 48 bit in which divided 8 bit header and 40 bit payload space. Realigned sinogram was generated from resampled event data of original listmode by using adjustment of LOR location, simple event magnification and nonparametric bootstrap. Sinogram was reconstructed for imaging using OSEM 2D algorithm with 16 subset and 4 iterations. Prompt coincidence was 13,940,707 count measured from PET data header and 13,936,687 count measured from analysis of list-event data. In simple event magnification of PET data, maximum was improved from 1.336 to 1.743, but noise was also increased. Resampling efficiency of PET data was assessed from de-noised and improved image by shift operation of payload value of sequential packet. Bootstrap resampling technique provides the PET image which noise and statistical properties was improved. List-event data resampling method would be aid to improve registration accuracy and early diagnosis efficiency.

• Korean Journal of Biological Psychiatry
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• v.28 no.2
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• pp.36-49
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• 2021

Suicide is a leading cause of death worldwide, especially among adolescents and young adults. Considering this fact, it is imperative that we understand the neural mechanisms underlying suicidal thoughts and behaviors in youth from a neurodevelopmental perspective. In this review, we focused on the magnetic resonance imaging studies that examined the neural correlates of suicidal ideations (SI) or attempts (SA) in youth. We reviewed twenty-three cross-sectional studies reporting the structural and functional alterations in association with SI or SA among adolescents and young adults with various mental disorders. The previous literature suggests that the dorsolateral prefrontal cortex, anterior cingulate cortex, and ventral frontolimbic circuit, may play an important role in the pathophysiology of suicidal behavior in youth through altered top-down control over emotion and impulsivity. Future studies with a longitudinal design and using multimodal imaging techniques may be of help to identify novel therapeutic targets specific for youth with suicidal thoughts and behaviors.

• Journal of the Optical Society of Korea
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• v.18 no.1
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• pp.55-59
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• 2014

Sarcopenia, or reduced muscle mass and volume, is due to various factors such as senile change, neuronal degeneration, drug, malignancy, and sepsis. Sarcopenia with the aging process has been evidenced by the decline in muscle mass by 0.5 to 1% per year with 3-5% reduction in muscle strength for 10 years between the ages of 40 and 50, and a 1-2% of decline of mass every year in people aged 60-70. Therefore, early diagnosis and understanding the mechanism of sarcopenia are crucial in the prevention of muscle loss. However, it is still difficult to image changes of muscle microstructure due to a lack of techniques. In this study, we developed an animal model using denervated rats to induce a rapid atrophy in the tibialis anterior (TA) and imaged its structural changes using optical coherence tomography (OCT) along with histologic and ultrasound analyses. Ultrasound showed changes of overall muscle size. Histology revealed that the atrophic TA muscle displayed an increased size variability of muscle fiber and inflammatory changes. Three dimensional OCT imaged the changes of perimysial grid and muscle fiber structure in real time without sacrifice. These observed advantages of multimodal imaging using OCT and ultrasound would provide clinical benefits in the diagnosis of sarcopenia.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.416-418
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• 2022

Glioma is a type of brain tumor that occurs in glial cells and is classified into two types: high hrade hlioma with a poor prognosis and low grade glioma. Magnetic resonance imaging (MRI) as a non-invasive method is widely used in glioma diagnosis research. Studies to obtain complementary information by combining multiple modalities to overcome the incomplete information limitation of single modality are being conducted. In this study, we developed a 3D CNN-based model that applied input-level fusion to MRI of four modalities (T1, T1Gd, T2, T2-FLAIR). The trained model showed classification performance of 0.8926 accuracy, 0.9688 sensitivity, 0.6400 specificity, and 0.9467 AUC on the validation data. Through this, it was confirmed that the grade of glioma was effectively classified by learning the internal relationship between various modalities.