• Journal of the Korean Society of Radiology
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• v.84 no.3
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• pp.536-549
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• 2023

The two main types of inflammatory bowel disease (IBD) are Crohn's disease and ulcerative colitis. Currently, when IBD is suspected, CT enterography is widely used as an initial imaging test because it can evaluate both the bowel wall and the outside of the bowel, helping to differentiate IBD from other diseases. When IBD is suspected, it is necessary to distinguish between Crohn's disease and ulcerative colitis. In most cases this is not difficult; however, in some cases, it is difficult and such cases are called IBD-unclassified. CT findings are often non-specific for ulcerative colitis, making it difficult to differentiate it from other diseases using imaging alone. In contrast, characteristic CT findings for Crohn's disease are often helpful in diagnosis, although diseases, such as tuberculous enteritis can mimic Crohn's disease. Recently, mutations in the gene encoding a prostaglandin transporter called SLCO2A1 have been discovered as the cause of the disease in some patients with multiple ulcers and strictures, similar to Crohn's disease. Therefore, genetic testing is being used to make a differential diagnosis.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.26-35
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• 1980

The degradation of image due to the finite size of sensing devices has been one of the problems in all digital imaging systems. The basic study on the improvement of the spatial resolution was carried out in both spatial and frequency domains by the resolution recovery techniques which have been used in optics. Here, the techniques were applied to CT (Computerized Tomography) system, and image with finer resolution was obtained by these techniques. The basic theory is described and the results of the simulation are shown.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.209-217
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• 2004

Evaluation of dementia in patients with early symptoms of cognitive decline is clinically challenging, but the need for early, accurate diagnosis has become more crucial, since several medication for the treatment of mild to moderate Alzheimer' disease are available. Many neurodegenerative diseases produce significant brain function alteration even when structural imaging (CT or MRI) reveal no specific abnormalities. The role of PET and SPECT brain imaging in the initial assessment and differential diagnosis of dementia is beginning to evolve vapidly and growing evidence indicates that appropriate incorporation of PET into the clinical work up can improve diagnostic and prognostic accuracy with respect to Alzheimer's disease, the most common cause of dementia in the geriatric population. in the fast few years, studios comparing neuropathologic examination with PET have established reliable and consistent accuracy for diagnostic evaluations using PET - accuracies substantially exceeding those of comparable studies of diagnostic value of SPECT or of both modalities assessed side by side, or of clinical evaluations done without nuclear imaging. This review deals the role of functional brain imaging techniques in the evaluation of dementias and the role of nuclear neuroimaging in the early detection and diagnosis of Alzheimer's disease.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.259-270
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• 2022

Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.

• Korean Chemical Engineering Research
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• v.57 no.4
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• pp.445-454
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• 2019

Layered double hydroxides (LDHs) nanoparticles have emerged as novel nanomaterials for bio-imaging applications due to its unique layered structure, physicochemical properties, and good biocompatibility. Bio-imaging is one of the most important fields for medical applications in clinical diagnostics and therapeutics of various diseases. Enhanced diagnostic techniques are needed to realize new paradigm for next-generation personalized medicine through nanoscale materials. When nanotechnology is introduced into bio-imaging system, nanoparticle probes can endow imaging techniques with enhanced ability to obtain information about biological system at the molecular level. In this review, we summarize structural features of LDH nanoparticles with current issues of bio-imaging system. LDH nanoparticle probes are also discussed through in vitro as well as in vivo studies in various bio-imaging techniques including fluorescence imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), and computed X-ray tomography (CT), which will have the potential in the development of the advanced nanoparticles with high sensitivity and selectivity.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Korean Journal of Radiology
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• v.20 no.6
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• pp.863-879
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• 2019

Hepatocellular carcinoma (HCC) can be noninvasively diagnosed on the basis of its characteristic imaging findings of arterial phase enhancement and portal/delayed "washout" on computed tomography (CT) and magnetic resonance imaging (MRI) in cirrhotic patients. However, different specific diagnostic criteria have been proposed by several countries and major academic societies. In 2018, major guideline updates were proposed by the Association for the Study of Liver Diseases, European Association for the Study of the Liver (EASL), Korean Liver Cancer Association and National Cancer Center (KLCA-NCC) of Korea. In addition to dynamic CT and MRI using extracellular contrast media, these new guidelines now include magnetic resonance imaging (MRI) using hepatobiliary contrast media as the first-line diagnostic test, while the KLCA-NCC and EASL guidelines also include contrast-enhanced ultrasound (CEUS) as the second-line diagnostic test. Therefore, hepatobiliary MR contrast media and CEUS will be increasingly used for the noninvasive diagnosis and staging of HCC. In this review, we discuss the emerging role of hepatobiliary phase MRI and CEUS for the diagnosis of HCC and also review the changes in the HCC diagnostic criteria in major guidelines, including the KLCA-NCC practice guidelines version 2018. In addition, we aimed to pay particular attention to some remaining issues in the noninvasive diagnosis of HCC.

• Journal of Gastric Cancer
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• v.11 no.3
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• pp.173-179
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• 2011

Purpose: It is difficult to obtain biopsies from gastrointestinal stromal tumors (GISTs) prior to surgery because GISTs are submucoal tumors, despite being the most common nonepithelial neoplasms of the gastrointestinal tract. Unlike anatomic imaging techniques, PET-CT, which is a molecular imaging tool, can be a useful technique for assessing tumor activity and predicting the malignant potential of certain tumors. Thus, we aimed to evaluate the usefulness of PET-CT as a pre-operative prognostic factor for GISTs by analyzing the correlation between the existing post-operative prognostic factors and the maximum SUV uptake (SUVmax) of pre-operative 18F-fluoro-2-deoxyglucose (FDG) PET-CT. Materials and Methods: The study was conducted on 26 patients who were diagnosed with gastric GISTs and underwent surgery after being examined with pre-operative FDG PET-CT. An analysis of the correlation bewteen (i) NIH risk classification and the Ki-67 proliferation index, which are post-operative prognostic factors, and (ii) the SUVmax of PET-CT, which is a pre-operative prognostic factor, was performed. Results: There were significant correlations between (i) SUVmax and (ii) Ki-67 index, tumor size, mitotic count, and NIH risk group (r=0.854, 0.888, 0.791, and 0.756, respectively). The optimal cut-off value for SUVmax was 3.94 between "low-risk malignancy" and "high-risk malignancy" groups. The sensitivity and specificity of SUVmax for predicting the risk of malignancy were 85.7% and 94.7%, respectively. Conclusions: The SUVmax of PET-CT is associated with Ki-67 index, tumor size, mitotic count, and NIH classification. Therefore, it is believed that PET-CT is a relatively safe, non-invasive diagnostic tool for assessing malignant potential pre-operatively.

• The Korean Journal of Gastroenterology
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• v.72 no.6
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• pp.277-280
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• 2018

Although small bowel the mainly occupies the most part of the gastrointestinal tract, small intestine tumors are rare, insidious in clinical presentation, and frequently represent a diagnostic and management challenge. Small bowel tumors are generally classified as epithelial, mesenchymal, lymphoproliferative, or metastatic. Familial adenomatous polyposis and Peutz-Jeghers syndrome are the most common inherited intestinal polyposis syndromes. Until the advent of capsule endoscopy (CE) and device-assisted enteroscopy (DAE) coupled with the advances in radiology, physicians had limited diagnostic examination for small bowel examination. CE and new radiologic imaging techniques have made it easier to detect small bowel tumors. DAE allows more diagnosis and deeper reach in small intestine. CT enteroclysis/CT enterography (CTE) provides information about adjacent organs as well as pictures of the intestinal lumen side. Compared to CTE, Magnetic resonance enteroclysis/enterography provides the advantage of soft tissue contrast and multiplane imaging without radiation exposure. Treatment and prognosis are tailored to each histological subtype of tumors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2480-2496
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• 2020

Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset-TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.