• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.370-377
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• 2014

At present trend 3D Printing technology has been using more efficiently than conventional subtractive manufacturing method in various medical fields, in particular this technology superior in saving production time, cost and process than conventional. Especially in orthopedics, an attractive attention has been paid by adopting this technology because of improving operation, operation accuracy, and reducing the patient's pain. Though 3D printing technology has enormous applications still in some hospitals have not been using due to having the problem of technical utilization of hardware, software & chiefly financial availability and etc. In order to solve these problems by reducing the cost and time, we have used CT images in pre-operative planning by directly making the pelvic fracture model with open source DICOM viewer and STL file conversion program, assembly 3D printer of FDM wire additive manufacturing. After having the customized bone model of six patients who underwent unstable pelvic fracture surgery, we have operated our system in orthopedic section of University Hospital through the clinician. Later, we have received better reviews and comments on utilization availability, results, and precision and now our system considered to be useful in surgical planning.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.27-34
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• 2018

Industrially, die-casting products are formed through casting, and so the methods to inspect the defects inside them are very restrictive. External inspection methods including visual inspection, sampling judgment, etc. enables researchers to inspect possible external defects, but x-ray inspection equipment has been generally used to inspect internal ones. Recently, they have been also applying three-dimensional internal inspections using CT equipment. However, they have their own limitations in applying to the use of industrial inspection due to limited detection size and long calculation time. To overcome the above problems, this paper has suggested a method to inspect internal defects by comparing the CAD data of the product to be inspected with the 3D data of the CT image. In this paper, we proposed a method for fast and accurate inspection in three dimensions by applying x-ray inspection to find internal defects in industrial parts such as aluminum die casting products. To show the effectiveness of the proposed method, a series of experiments have been carried out.

• 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.

• Journal of the Korean Society of Radiology
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• v.7 no.5
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• pp.359-364
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• 2013

Femur is the largest bone in the human body which supports the weight of body. A long pipeline shape of femur has little cancellous bone, so that regeneration is difficult when fracture happens. The fracture caused by an accident most frequently occurs at diaphysis. IM Nailing is the surgical method that implants an IM Nail into a medullary cavity for the fixation of fracture parts. However, a secondary fracture may happen if an IM Nail does not penetrate at the center of femur. In this study, a patient-specific femur was manufactured by a 3D printer using the computed tomography images scanned before surgery, which was used for the simulation of IM Nailing. It is expected that this result may prevent the secondary damage, reduce surgical operation time, and increase the precision.

• Computers and Concrete
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• v.18 no.3
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• pp.319-336
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• 2016

In this study, both two- and three-dimensional (2D and 3D) finite-volume-based models were developed to analyze the heat transfer mechanisms through the porous structures of cellular concretes under steady-state heat transfer conditions and to investigate the differences between the 2D and 3D modeling results. The 2D and 3D reconstructed pore networks were generated from the microstructural information measured by 3D images captured by X-ray computerized tomography (X-CT). The computed effective thermal conductivities based on the 2D and 3D calculations performed on the reconstructed porous structures were found to be nearly identical to those evaluated from the 2D cross-sectional images and the 3D X-CT images, respectively. In addition, the 3D computed effective thermal conductivity was found to agree better with the measured values, in comparison with the 2D reconstruction and real cross-sectional images. Finally, the thermal conductivities computed for different reconstructed porous 3D structures of cellular concretes were compared with those obtained from 2D computations performed on 2D reconstructed structures. This comparison revealed the differences between 2D and 3D image-based modeling. A correlation was thus derived between the results of the 3D and 2D models.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.315-325
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• 2023

The purpose of this study was to predict abdominal obesity with 3-Dimensional computed tomography (3D CT) measurements of kidneys by analyzing the correlation between kidney sizes and abdominal obesity level. The subjects were 178 healthy adults without underlying diseases who had a comprehensive health examination at the Health Medical Center of Jesus Hospital in Jeonju. Abdominal obesity was measured by CT cross-sectional image at the level of the umbilicus and divided into visceral fat area, subcutaneous fat area, visceral fat/total fat ratio. The average comparison of kidney sizes classified according to abdominal obesity were performed through one-way analysis of variance (ANOVA) and Scheffe test. Pearson correlation analysis was performed to correlate all measurement values. The results of kidney size ANOVA analysis according to abdominal obesity were as follows. The means of kidney measurements according to visceral fat classification were significantly different in all kidney measurements (p<0.05). And in case of subcutaneous fat classification, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in the right kidney width (p<0.05). In case of visceral fat area/total fat area ratio, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in both kidneys width (p<0.05). Pearson correlation between kidneys measurements and CT abdominal obesity showed that visceral fat area had the highest correlation with the left kidney width measured by 3D CT (r=0.467) and subcutaneous fat area had correlation with the right kidney width measured by 3D CT (r=0.249). The visceral fat area/total fat area ratio had correlation with the left kidney width measured by 3D CT (r=0.291).

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.227-235
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• 2012

The void distribution in concrete materials strongly affects its material properties. Therefore, the identification of spatial distribution of void is important to understand and estimate material behavior. To examine and quantify the void distribution inside lightweight aggregates, CT(computed tomography) image is used. 3D lightweight aggregate images are generated by stacking of cross-sectional images from CT. Spatial distribution of void of aggregate along the direction is visualized on the sphere using probability distribution function. Stiffness of lightweight aggregate for the directions is also examined. It is confirmed that direction-based probability distribution and stiffness from CT images are effective in characterizing void distributions of aggregates.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.97-102
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• 2004

We developed an x-ray cone-beam micro computed tomography (micro-CT) system for small-animal imaging. The micro-CT system consists of a 2-D flat-panel x-ray detector with a field-of-view (FOV) of 120${\times}$120 mm2, a micro-focus x-ray source, a scan controller and a parallel image reconstruction system. Imaging performances of the micro-CT system have been evaluated in terms of contrast and spatial resolution. The minimum resolvable contrast has been found to be less than 36 CT numbers at the dose of 95 mGy and the spatial resolution about 14 lp/mm. As small animal imaging results, we present high resolution 3-D images of rat organs including a femur, a heart and vessels. We expected that the developed micro-CT system can be greatly used in biomedical studies using small animals.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.435-441
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• 2021

Objective: To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. Materials and Methods: Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. Results: Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. Conclusion: The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.