• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.154-161
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• 1997

Purpose: Traumatic rupture of the diaphragm is not easy to diagnose and often delayed. Delayed diagnosis of diaphragmatic rupture accompanied by higher chances of strangulation of herniated viscera which may result in higher morbility and mortality. The purpose of this study was to evaluate diagnostic accuracy of spiral CT, MRI and US for the diagnosis of diaphragmatic rupture in an animal model. Materials and Methods: Small, medium, and large sized transabdominal diaphragmatic ruptures were surgically made in experimental rabbits and then followed up with spiral CT, MR!, and US at 1 day, 3 day, and 1 week after operation. Results: US was superior to MR! or spiral CT in diagnosis of diaphragmatic rupture(P(0.05). The sensitivity and specificity were 94.4% and 92.9% for US, 54.0% and 85.7% for MRI, and 46.0% and 78.6% for spiral CT, respectively. The size of laceration was not related to diagnostic sensitivity in US. Sensitivity of MRI and spiral CT increased as the size of laceration were larger, but no statistical significance was present(P>0.05). All experimental animals developed pleural effusion or hemothorax one day after operation. In acute phase, US and MRI were more sensitive than spiral CT in detecting diaphragmatic rupture. Spiral CT was more sensitive than US and MRI in delayed phase but without statistical significance(P>0.05). In the experimental rabbits with accompanying visceral hernia through the diaphragmatic defect, diagnostic accuracy was found equally high among three image modalities(P>0.05). Conclusion: This study indicates that US is the most accurate diagnostic method in detecting injury to the diaphragm in a rabbit model. The findings obtained in this experimental study can be applied to the diaphragmatic rupture of human being.

• Progress in Medical Physics
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• v.4 no.2
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• pp.19-25
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• 1993

Treatment planning of lung cancer with density corrected Computed tomography. Eighty-seven patients with lung cnacer who had radiation therapy in Yeungnam University Medical Center between, April 1 1990 and Aug. 30 1993 were retrospectively evaluated total tumor dose, dose distribution, field correction, and loading change, compared with contour or CT image planning and density corrected CT planning. In dose distribution, higher dose was calculated in compare with density corrected CT planning less than 5% difference were found in 45 patient(52%), 5-10% in 25 patients (29%), 10-15% in 15 patients (17%) and over 15% in 2 patients (2%). Correction of treatment field was performed in 18 patients (21%) and changing of dose loading was given in 15 patients (17%). In conclusion, we emphasize that density corrected CT planning is the very important factor which contribute to increase therapeutic gain by exact selection of target volume, target dose, normal tissue dose and dose of critical organ.

• Progress in Medical Physics
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• v.24 no.2
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• pp.99-107
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• 2013

The surgical resection was occurred mainly in liver metastasis before the development of radiation therapy techniques. Recently, Radiation therapy is increased gradually due to the development of radiation dose delivery techniques. 18F-FDG PET image showed better sensitivity and specificity in liver metastasis detection. This image modality is important in the radiation treatment with planning CT for tumor delineation. In this study, we applied automatic image segmentation methods on PET image of liver metastasis and examined the impact of image factors on these methods. We selected the patients who were received the radiation therapy and 18F-FDG PET/CT in Korea Cancer Center Hospital from 2009 to 2012. Then, three kinds of image segmentation methods had been applied; The relative threshold method, the Gradient method and the region growing method. Based on these results, we performed statistical analysis in two directions. 1. comparison of GTV and image segmentation results. 2. performance of regression analysis for relation between image factor affecting image segmentation techniques. The mean volume of GTV was $60.9{\pm}65.9$ cc and the $GTV_{40%}$ was $22.43{\pm}35.27$ cc, and the $GTV_{50%}$ was $10.11{\pm}17.92$ cc, the $GTV_{RG}$ was $32.89{\pm}36.8$4 cc, the $GTV_{GD}$ was $30.34{\pm}35.77$ cc, respectively. The most similar segmentation method with the GTV result was the region growing method. For the quantitative analysis of the image factors which influenced on the region growing method, we used the standardized coefficient ${\beta}$, factors affecting the region growing method show GTV, $TumorSUV_{MAX/MIN}$, $SUV_{max}$, TBR in order. The result of the region growing (automatic segmentation) method showed the most similar result with the CT based GTV and the region growing method was affected by image factors. If we define the tumor volume by the auto image segmentation method which reflect the PET image parameters, more accurate and consistent tumor contouring can be done. And we can irradiate the optimized radiation dose to the cancer, ultimately.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.51-61
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• 2004

In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

• Journal of Radiation Industry
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• v.11 no.3
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• pp.131-137
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• 2017

To compare the radiation dose and image noise of low dose computed tomography (CT) and high resolution CT using the fixed tube current technique and automatic tube current modulation (CARE Dose 4D). Chest CT and human anthropomorphic phantom were used the RPL (radiophotoluminescence) dosimeters. For image evaluation, standard deviation of mean CT attenuation coefficient and CT attenuation coefficient was measured using ROI analysis function. The effective dose was calculated using CTDIvol and DLP. CARE Dose 4D was reduced by 74.7% and HRCT by 64.4% compared to the fixed tube current technique in low dose CT of chest phantom. In CTDIvol and DLP, the dose of CARE Dose 4D was reduced by fixed tube current technique. For effective dose, CARE Dose 4D was reduced by 47% and HRCT by 46.9% compared to the fixed tube current method, and the dose of CARE Dose 4D was significantly different (p<.05). Noise in the image was higher than that in the fixed tube current technique. Noise difference in the image of CARE Dose 4D in low dose CT was significant (p<.05). The low radiation dose and the noise difference of the CARE Dose 4D were compared with the fixed tube current technique in low dose CT and HRCT using chest phantom. The radiation doses using CARE Dose 4D were in accordance with the national and international dose standards. CARE Dose 4D should be applied to low dose CT and HRCT for clinical examination.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Journal of radiological science and technology
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• v.41 no.3
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• pp.209-214
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• 2018

The results of CBCT was obtained using image guided radiation therapy for radiation therapy in 5 prostate cancer patients. Using these results, we compared and evaluated the dose changes according to the treatment plan depending on the volume and position of bladder, rectum, and prostate. The 28 images of CBCT were acquired using On-Board Imaging device before radiotherapy. After the outline of bladder, rectum, and PTV, pCT images and CBCT images for radiotherapy were treated respectively. The volume of the bladder was increased by 105.6% and decreased by 45.2%. The volume of the rectum was increased by 30.5% and decreased by 20.3%. Prostate volume was increased by 6.3% and decreased by 12.3%. The mean dose of the rectum was higher in the CBCT than in the pCT, and V40 (equivalent to 40 Gy) of the bladder showed a reduction in all treatment regimens in the CBCT than in the pCT. Conformity treatment and homogeneity index of PTV showed better results in all treatment regimens using pCT than CBCT. It was found that the dose distribution of the pelvic internal organs varied greatly according to the patient 's condition and pretreatment.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.655-661
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• 2017

Sparse view CT has been widely used to reduce radiation dose to patient in radiation therapy. In this work, we performed sinogram restoration from sparse sampling data by using inpainting method for simulation and experiment. Sinogram restoration was performed in accordance with sampling angle and restoration method, and their results were validated with root mean square error (RMSE) and image profiles. Simulation and experiment are designed to fan beam scan for various projection angles. Sparse data in sinogram were restored by using linear interpolation and inpainting method. Then, the restored sinogram was reconstructed with filtered backprojection (FBP) algorithm. The results showed that RMSE and image profiles were depended on the projection angles and restoration method. Based on the simulation and experiment, we found that inpainting method could be improved for sinogram restoration in comparison to linear interpolation method for estimating RMSE and image profiles.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.39-42
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• 2000

In our study, by observing and analyzing normal liver in abdominal CT image, we estimated gray value range and generated binary image. In the binary image, we achieved the number of hole which is located between pixels. Depending on the ratio, we processed the input image to 4 kinds of mesh images to remove the noise part that has the different ratio. With the Union image of 4 kinds of mesh images, we generated the template representing general outline of liver and subtracted from the binary image so the we can represent the organ boundary to be minute. With results of proposed method, processing time is reduced compared with existing method and we compared the result image to manual image of medical specialists.

• Progress in Medical Physics
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• v.17 no.4
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• pp.207-211
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• 2006

The advent of kV cone-beam computed tomography (CBCT) integrated with a linear accelerator allows for more accurate Image-guided radiotherapy (IGRT). IGRT is the technique that corrects target displacement based on internal body information. To do this, the CBCT Image set is acquired just before the beam is delivered and registered with the simulation CT Image set. In this study, we compare the registration results according to the CBCT's reconstruction quality (either high or medium). A total of 56 CBCT projection data from 6 patients were analyzed. The translation vector differences were within 1 mm in all but 3 cases. For rotation displacement difference, components of all three axes were considered and 3 out of 168 ($56{\times}3$ axes) cases showed more than lo of rotation differences.