• 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 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.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.12
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• pp.1901-1908
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• 2007

The aim of this series of experiments was to examine the opportunity for application of X-ray computer tomography (CT) in cattle production. Firstly, tissue composition of M. longissimus dorsi (LD) cuts between the $11-13^{th}$ ribs (in Exp 1. between the $9-11^{th}$ ribs), was determined by CT and correlated with tissue composition of intact half carcasses prior to dissection and tissue separation. Altogether, 207 animals of different breeds and genders were used in the study. In Exp. 2 and 3, samples were taken from LD cuts, dissected and chemical composition of muscle homogenates was analysed by conventional procedures. Correlation coefficients were calculated among slaughter records, tissues in whole carcasses and tissue composition of rib samples. Results indicated that tissue composition of rib samples determined by CT closely correlated with tissue composition results by dissection of whole carcasses. The findings revealed that figures obtained by CT correlate well with the dissection results of entire carcasses (meat, bone, fat). Close three-way coefficients of correlation (r = 0.80-0.97) were calculated among rib eye area, volume of cut, pixel-sum of adipose tissue determined by CT and intramuscular fat or adipose tissue in entire carcasses. Estimation of tissue composition of carcasses using equations including only CT-data as independent variables proved to be less reliable in prediction of lean meat and bone in carcass ($R^2 = 0.51-0.86$) than for fat (($R^2 = 0.83-0.89$). However, when cold half carcass weight was also included in the equation, the coefficient of determination exceeded $R^2 = 0.90$. In Exp. 3 tissue composition of rib samples by CT were compared to the results of EUROP carcass classification. Findings revealed that CT analysis has higher predictive value in estimation of actual tissue composition of cattle carcasses than EUROP carcass classification.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.890-902
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• 2023

Objective: The clinical impact of artificial intelligence-based computer-aided detection (AI-CAD) beyond diagnostic accuracy remains uncertain. We aimed to investigate the influence of the clinical implementation of AI-CAD for chest radiograph (CR) interpretation in daily practice on the rate of referral for chest computed tomography (CT). Materials and Methods: AI-CAD was implemented in clinical practice at the Seoul National University Hospital. CRs obtained from patients who visited the pulmonology outpatient clinics before (January-December 2019) and after (January-December 2020) implementation were included in this study. After implementation, the referring pulmonologist requested CRs with or without AI-CAD analysis. We conducted multivariable logistic regression analyses to evaluate the associations between using AI-CAD and the following study outcomes: the rate of chest CT referral, defined as request and actual acquisition of chest CT within 30 days after CR acquisition, and the CT referral rates separately for subsequent positive and negative CT results. Multivariable analyses included various covariates such as patient age and sex, time of CR acquisition (before versus after AI-CAD implementation), referring pulmonologist, nature of the CR examination (baseline versus follow-up examination), and radiology reports presence at the time of the pulmonology visit. Results: A total of 28546 CRs from 14565 patients (mean age: 67 years; 7130 males) and 25888 CRs from 12929 patients (mean age: 67 years; 6435 males) before and after AI-CAD implementation were included. The use of AI-CAD was independently associated with increased chest CT referrals (odds ratio [OR], 1.33; P = 0.008) and referrals with subsequent negative chest CT results (OR, 1.46; P = 0.005). Meanwhile, referrals with positive chest CT results were not significantly associated with AI-CAD use (OR, 1.08; P = 0.647). Conclusion: The use of AI-CAD for CR interpretation in pulmonology outpatients was independently associated with an increased frequency of overall referrals for chest CT scans and referrals with subsequent negative results.

• Tuberculosis and Respiratory Diseases
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• v.57 no.6
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• pp.515-521
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• 2004

With the increasing resolution of modern CT scanners, analysis of the larger numbers of images acquired in a lung screening exam or diagnostic study is necessary, which also needs high accuracy and reproducibility. Recent developments in the computerized analysis of medical images are expected to aid radiologists and other healthcare professional in various diagnostic tasks of medical image interpretation. This article is to provide a brief overview of some of computer-aided diagnosis schemes in chest CT.

• Journal of IKEEE
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• v.23 no.3
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• pp.768-776
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• 2019

With the deployment of 4-th generation industrial revolution, the computer based manufacturing technologies employing advanced IT technology are much more popular than any other past years. In this research, some novel S/W technologies related to the industrial X-ray CT (computed tomography) for the inspection of the industrial parts are introduced. First, newly constructed industrial X-ray CT is presented in this paper, where some basic principles and functions of the CT are described. Then some research platforms are developed to generate more advanced functionalities of the industrial CT. Especially, the data transform from CT to general S/W such as Matlab is conducted. And based on this techniques, some supplementary S/W platform such as GUI (graphical user interface) of the CT S/W and some 3D voxel based image processing technologies can be developed in this paper. The industrial CT is one of the rare research items and it's values can be much more enhanced when it is used with advanced IT technologies.

• 동위원소뉴스
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• no.3 s.3
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• pp.8-8
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• 1997

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.859-861
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• 2005

X-ray를 이용한 CT(Computed Tomography : 이하 CT)영상은 사물에 대해 회전하면서 X-ray가 투과하여 감약 정도에 따라서 영상을 획득하지만 검사 목적과는 관계없이 발생되는 통계적인 오차로 인해 정확한 CT영상의 구성을 교란하거나 방해하여 영상의 질을 저하시키고 미세 부분의 관찰 능력을 감소시키는 장해 음영인 아티팩트(artifact)라는 노이즈가 발생한다. 이러한 노이즈를 제거하는 필터를 설계 할 때는 두 가지 고려해야 할 사항이 있는데 첫째는 영상내의 노이즈을 정확히 판단하여 효과적으로 제거해야 하며, 둘째로는 원래의 영상에 가깝도록 경계와 같은 세부 영역을 보존해야 한다는 점이다. 기존에는 mean 필터나 median 필터, 그리고 Gaussian 필터 등을 사용했지만 상세한 부분을 보존하기에는 실패하는 단점이 있다. 따라서 본문에서는 wavelet 변환을 하여 영상의 주파수 대역을 저주파 영역과 고주파 영역으로 분리하여 각각의 영역에서 노이즈를 제거할 수 있도록 적합한 필터를 설계하고 방법을 제안하여 그 필터를 CT 3차원 뇌혈관 영상에 적용하여 많은 노이즈를 제거하였고 낮은 Threshold값에서도 작은 혈관을 관찰 할 수 있었다.

• The Magazine of the IEIE
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• v.8 no.1
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• pp.16-32
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• 1981

물리학적인 측면과 공학적인 측면에서의 CT(computerized tomography)의 최근 동향과 발전 과정을 되돌아 보고 의료 현장에서 실제 사용되고 있거나 아직 연구되고 있는 CTsystem의 성능과 그 특성에 대해 기술해 보았다. 또한 CT의 역사적인 발전과정, tomophysics 및 chemistry, system의 구성, detector 및 sensing diode, 신호의 수집과 처리를 위한 전자회로, computer system 및 주변 연산장치등에 대해서 고찰하였다.

• 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.