• Journal of radiological science and technology
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• v.38 no.4
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• pp.383-387
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• 2015

This study investigated the size specific dose estimates of difference localizer on pediatric CT image. Seventy one cases of pediatric abdomen-pelvic CT (M:F=36:35) were included in this study. Anterior-posterior and lateral diameters were measured in axial CT images. Conversion factors from American Association of Physicists in Medicine (AAPM) report 204 were obtained for effective diameter to determine size specific dose estimate (SSDE) from the CT dose index volume (CTDIvol) recorded from the dose reports. For the localizer of mid-slice SSDE was 107.63% higher than CTDIvol and that of xiphoid-process slices SSDE was higher than 92.91%. The maximum error of iliac crest slices, xiphoid process slices and femur head slices between mid-slices were 7.48%, 17.81% and 14.04%. In conclusion, despite the SSDE of difference localizer has large number of errors, SSDE should be regarded as the primary evaluation tool of the patient radiation in pediatric CT for evaluation.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.71-74
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• 1990

In the medical modalities, three-dimensional objects must be reconstructed from the consecutive slices. but the slime separation is usually much greater than the pixel size within an individual slices. In this paper, an interpolation scheme for filling the spare between the shapes in two successive slices is developed. It minimizes the computation involvement in segmentation of 3-D reconst ructlon process as well as more accurately approximates the object than the linear interpolation method.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.450-461
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• 2020

Objective: We performed a meta-analysis to evaluate the agreement of cardiac computed tomography (CT) with cardiac magnetic resonance imaging (CMRI) in the assessment of right ventricle (RV) volume and functional parameters. Materials and Methods: PubMed, EMBASE, and Cochrane library were systematically searched for studies that compared CT with CMRI as the reference standard for measurement of the following RV parameters: end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), or ejection fraction (EF). Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient (r) between CT and CMRI. Heterogeneity was also assessed. Subgroup analyses were performed based on the probable factors affecting measurement of RV volume: CT contrast protocol, number of CT slices, CT reconstruction interval, CT volumetry, and segmentation methods. Results: A total of 766 patients from 20 studies were included. Pooled bias and LOA were 3.1 mL (-5.7 to 11.8 mL), 3.6 mL (-4.0 to 11.2 mL), -0.4 mL (5.7 to 5.0 mL), and -1.8% (-5.7 to 2.2%) for EDV, ESV, SV, and EF, respectively. Pooled correlation coefficients were very strong for the RV parameters (r = 0.87-0.93). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, an RV-dedicated contrast protocol, ≥ 64 CT slices, CT volumetry with the Simpson's method, and inclusion of the papillary muscle and trabeculation had a lower pooled bias and narrower LOA. Conclusion: Cardiac CT accurately measures RV volume and function, with an acceptable range of bias and LOA and strong correlation with CMRI findings. The RV-dedicated CT contrast protocol, ≥ 64 CT slices, and use of the same CT volumetry method as CMRI can improve agreement with CMRI.

• 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.

• The Journal of Korean Society for Radiation Therapy
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• v.12 no.1
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• pp.20-25
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• 2000

The accuracy of the target localization was evaluated by conventional and spiral CT in stereotactic radiosurgerv. Conventional and spiral CT images were obtained with geometrical phantom, which was designed to produce exact three-dimensional coordinates of several objects within 0.1mm error range. Geometrical phantom was attached by BRW headframe, intermediate head ring, and CT localizer. Twentv-seven slices of conventional CT image were scanned at 3 mm slice thickness. Spiral CT images were scanned at 3 mm slice thickness from the pitch value 1 to 3, and twenty-seven slices of image were obtained per each the pitch value. These CT images were transferred to a treatment planning system(X-knife, Radionics) by ethernet, Three-dimensional coordinates of these images measured from the treatment planning system were compared to known values of geometrical phantom. The mean localization error of the target localization of conventional CT was 1.4mm. In case of spiral CT, the error of the target localization was within 1.6mm from the pitch value 1 to 1.3, but was more than 30mm above the pitch value 1.5. In conclusion, as the localization error of spiral CT was increased in high pitch value compared to conventional CT, the application of spiral CT will be with caution in stereotactic radiosurgery.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.171-176
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• 2005

This paper proposes an automatic liver segmentation method using improved partial histogram threshold (PHT) algorithms. This method removes neighboring abdominal organs regardless of random pixel variation of contrast enhanced CT images. Adaptive multi-modal threshold is first performed to extract a region of interest (ROI). A left PHT (LPHT) algorithm is processed to remove the pancreas, spleen, and left kidney. Then a right PHT (RPHT) algorithm is performed for eliminating the right kidney from the ROI. Finally, binary morphological filtering is processed for removing of unnecessary objects and smoothing of the ROI boundary. Ten CT slices of six patients (60 slices) were selected to evaluate the proposed method. As evaluation measures, an average normalized area and area error rate were used. From the experimental results, the proposed automatic liver segmentation method has strong similarity performance as the MSM by medical Doctor.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.24 no.2
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• pp.361-368
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• 1994

Radiographic planning is needed for implant placement in order to determine implant length, jaw bone volume, anatomical stucture and so on. Radiographic examination includes conventional radiography, conventional tomography and CT scan. The most accurate mesurement can be obtained from CT scan. For the cross-sectional view of mandible, CT scan reconstruction is generally needed. But the cross-sectional view of mandible can be reformed by personal computer. This study was performed to examine the clinical usefulness of reformed image using personal computer in comparison with CT scan reconstructed image. CT axial slices of 4 mandibles of 4 volunteers were used. Digital imaging system was composed of Macintosh Ⅱ ci computer, high resolution Sony XC-77 CCD camera, Quick Capture frame grabber board and 'NIH Image' program. Seven reconstructed cross-sectional images within CT machine(CT group) were obtained. And seven reformed cross-sectional images(PC group) after digitization of CT axial slices into the personal computer were obtained. PC group was compared with CT group in the objective and subjective aspects. The results were as follow: 1. Measurement of mandibular height & width in both group showed insignificant difference(P>0.05). 2. Subjective assessment of the mandibular canal in both group showed insignificant difference(P>0.05). 3. Image reformation using personal computer could provide panoramic view, which could not be obtained in CT scan reconstruction.

• Tuberculosis and Respiratory Diseases
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• v.55 no.3
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• pp.239-249
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• 2003

Multi-detector row CT (MDCT) provides faster speed, longer coverage in conjunction with thin slices, improved spatial resolution, and ability to produce high quality muliplanar and three-dimensional (3D) images. MDCT has revolutionized the non-invasive evaluation of the central airways. Simultaneous display of axial, multiplanar, and 3D images raises precision and accuracy of the radiologic diagnosis of central airway disease. This article introduces central airway imaging with MDCT emphasizing on the emerging role of multiplanar and 3D reconstruction.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.163-174
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• 2004

The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.