• Progress in Medical Physics
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• v.32 no.1
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• pp.1-17
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• 2021

The evolution of X-ray computed tomography (CT) has been based on the discovery of X-rays, the inception of the Radon transform, and the development of X-ray digital data acquisition systems and computer technology. Unlike conventional X-ray imaging (general radiography), CT reconstructs cross-sectional anatomical images of the internal structures according to X-ray attenuation coefficients (approximate tissue density) for almost every region in the body. This article reviews the essential physical principles and technical aspects of the CT scanner, including several notable evolutions in CT technology that resulted in the emergence of helical, multidetector, cone beam, portable, dual-energy, and phase-contrast CT, in integrated imaging modalities, such as positron-emission-tomography-CT and single-photon-emission-computed-tomography-CT, and in clinical applications, including image acquisition parameters, CT angiography, image adjustment, versatile image visualizations, volumetric/surface rendering on a computer workstation, radiation treatment planning, and target localization in radiotherapy. The understanding of CT characteristics will provide more effective and accurate patient care in the fields of diagnostics and radiotherapy, and can lead to the improvement of image quality and the optimization of exposure doses.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.35-42
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• 2018

Direct volume rendering (DVR) is an important 3D visualization method for medical images as it depicts the full volumetric data. However, because DVR renders the whole volume, regions of interests (ROIs) such as a tumor that are embedded within the volume maybe occluded from view. Thus, conventional 2D cross-sectional views are still widely used, while the advantages of the DVR are often neglected. In this study, we propose a new visualization algorithm where we augment the 2D slice of interest (SOI) from an image volume with volumetric information derived from the DVR of the same volume. Our occlusion-based DVR augmentation for SOI (ODAS) uses the occlusion information derived from the voxels in front of the SOI to calculate a depth parameter that controls the amount of DVR visibility which is used to provide 3D spatial cues while not impairing the visibility of the SOI. We outline the capabilities of our ODAS and through a variety of computer tomography (CT) medical image examples, compare it to a conventional fusion of the SOI and the clipped DVR.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.834-850
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• 2011

In this paper, a novel tissue engineering scaffold design method based on triply periodic minimal surface (TPMS) is proposed. After generating the hexahedral elements for a 3D anatomical shape using the distance field algorithm, the unit cell libraries composed of triply periodic minimal surfaces are mapped into the subdivided hexahedral elements using the shape function widely used in the finite element method. In addition, a heterogeneous implicit solid representation method is introduced to design a 3D (Three-dimensional) bio-mimetic scaffold for tissue engineering from a sequence of computed tomography (CT) medical image data. CT image of a human spine bone is used as the case study for designing a 3D bio-mimetic scaffold model from CT image data.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.167-171
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• 2004

The evolution of modern imaging modalities, followed by the rapid development of computer technology has introduced many new features in the communication networks used in medical facilities. Since it is very important to keep patient's record accurately, the ability to exchange medical data securely over the communication network is essential for any medical information. In this paper, therefore, we introduce some problems which occur from digitizing medical images such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), CR(Computed Radiography), etc., and then we propose a authentication mechanism for medical image verification using secret watermark images.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.41-47
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• 2019

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were $0.905{\pm}0.048$ and $0.908{\pm}0.047$. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• Imaging Science in Dentistry
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• v.34 no.1
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• pp.13-18
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• 2004

Purpose : This study was to evaluate the influence of slice thickness of computed tomography (CT) and rapid protyping (RP) type on the accuracy of 3-dimensional medical model. Materials and Methods: Transaxial CT data of human dry skull were taken from multi-detector spiral CT. Slice thickness were 1, 2, 3 and 4 mm respectively. Three-dimensional image model reconstruction using 3-D visualization medical software (V-works /sup TM/ 3.0) and RP model fabrications were followed. 2-RP models were 3D printing (Z402, Z Corp., Burlington, USA) and Stereolithographic Apparatus model. Linear measurements of anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared according to slice thickness and RP model type. Results: There were relative error percentage in absolute value of 0.97, 1.98,3.83 between linear measurements of dry skull and image models of 1, 2, 3 mm slice thickness respectively. There was relative error percentage in absolute value of 0.79 between linear measurements of dry skull and SLA model. There was relative error difference in absolute value of 2.52 between linear measurements of dry skull and 3D printing model. Conclusion: These results indicated that 3-dimensional image model of thin slice thickness and stereolithographic RP model showed relative high accuracy.

• The Transactions of the Korea Information Processing Society
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• v.5 no.12
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• pp.3275-3284
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• 1998

In this paper, we deseribe a Medical Image Information System. Our system stores and manages 5 dimensional medical image data and provides the 3 dimensional medical data via the Internet. The Internet standard VR format. VRML(Virtual Reality Modeling Language) is used to represent the 3I) medical image data. The 3D images are reconstructed from medical image data which are enerated by medical imaging systems such ans CT(Computerized Tomography). MRI(Magnetic Resonance Imaging). PET(Positron Emission Tomograph), SPECT(Single Photon Emission Compated Tomography). We implemented the medical image information system shich rses a surface-based rendering method for the econstruction of 3D images from 2D medical image data. In order to reduce the size of image files to be transfered via the Internet. The system can reduce more than 50% for the triangles which represent the surfaces of the generated 3D medical images. When we compress the 3D image file, the size of the file can be redued more than 80%. The users can promptly retrieve 3D medical image data through the Internet and view the 3D medical images without a graphical acceleration card, because the images are represented in VRML. The image data are generated by various types of medical imaging systems such as CT, MRI, PET, and SPECT. Our system can display those different types of medical images in the 2D and the 3D formats. The patient information and the diagnostic information are also provided by the system. The system can be used to implement the "Tele medicaine" systems.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• 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.