X-ray CT Scans - Set 1

X-ray Computed Tomography (CT) is a powerful non-destructive technique that can yield interesting structural information not discernible through visual examination only. This paper presents the results of the CT scans of four objects belonging to the Romanian cultural heritage attributed to the Vinča, Cucuteni and Cruceni-Belegiš cultures. The study was performed with an X-ray tomographic device developed at the Department for Applied Nuclear Physics from Horia Hulubei National Institute for Nuclear Physics and Engineering in Măgurele, Romania. This apparatus was specially designed for archaeometric studies of low-Z artifacts: ceramic, wood, bone. The tomographic investigations revealed the internal configuration of the objects and provided information about the degree to which the previous manipulations affected the archaeological items. Based on the X-ray images resulting from the CT scans, hints about the techniques used in the manufacturing of the artifacts were obtained, as well as some indications useful for conservation/restoration purposes.

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Motion correction for routine X-ray lung CT imaging

Scientific Reports ◽

10.1038/s41598-021-83403-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Doil Kim ◽

Jiyoung Choi ◽

Duhgoon Lee ◽

Hyesun Kim ◽

Jiyoung Jung ◽

...

Keyword(s):

Motion Estimation ◽

Motion Correction ◽

Ct Imaging ◽

Motion Artifacts ◽

Ct Scans ◽

Breath Hold ◽

X Ray ◽

Wide Range ◽

Thorax Ct ◽

Lung Ct

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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Congenital Vertebral Malformations In French Bulldogs: X-Ray Vs Computed Tomography

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca Veterinary Medicine ◽

10.15835/buasvmcn-vm:11508 ◽

2017 ◽

Vol 74 (1) ◽

pp. 123

Author(s):

Lucia Madalina CORLAT ◽

B. BLANCO ◽

R. LUCERNA ◽

P. J. GINEL ◽

F. MIRO ◽

...

Keyword(s):

Computed Tomography ◽

Vertebral Body ◽

Ct Scans ◽

Spinal Level ◽

X Ray ◽

Clinical Evolution ◽

Important Impact ◽

Degree Of Certainty ◽

French Bulldog ◽

Superposition Effect

Congenital vertebral malformations of the thoracolumbar area can have an important impact in the clinical evolution of French Bulldogs due to the instability it creates at the spinal level. The aim of this study is to show the differences between x-ray and CT scans in vertebral malformations of the French Bulldog. CT scans can offer a higher degree of certainty in the diagnosis of congenital vertebral malformations of the dogs. The VR model can offer a more thorough evaluation of the existing modifications of the vertebral body, allowing the examiner to circumvent the superposition effect than can be observed in the x-ray views and offering the chance to evaluate whether there is scoliosis or kyphosis present.

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Head, Examined: Clarence Darrow's X-Ray Vision of Criminal Responsibility

The Journal of Psychiatry & Law ◽

10.1177/009318531103900406 ◽

2011 ◽

Vol 39 (4) ◽

pp. 627-661 ◽

Cited By ~ 1

Author(s):

Kenneth J. Weiss

Keyword(s):

19Th Century ◽

Criminal Responsibility ◽

Scientific Evidence ◽

Ct Scans ◽

X Rays ◽

Pathological Anatomy ◽

X Ray ◽

Murder Trial ◽

Scientific Arguments ◽

Penalty Phase

Shortly after Roentgen's discovery of X-rays and their application to human imaging, the legal profession began to use the technology in litigation. Though the use of brain imaging did not find its way into formal arguments about criminal responsibility early in its evolution, such an analysis has been sought. 19th Century attempts to connect “pathological anatomy” to behavior were mostly disappointing. In 1924, the celebrated murder trial of Leopold and Loeb in Chicago became an early example of the use of scientific testimony that included radiographic exhibits. The penalty-phase decision to spare the defendants' lives was not based on scientific arguments. Sixty years later, the trial of John Hinckley included admission of CT scans to aid psychiatric testimony. Using excerpts from the expert reports and testimony, this article examines the nature and purpose of scientific evidence pertaining to blameworthiness. The author concludes that improvements in neuroimaging will continue to force a dialog between science and the law.

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X-ray CT Scans - Set 2

10.2172/1644168 ◽

2020 ◽

Author(s):

John Korbin ◽

Anna Bancroft ◽

Jonathan Dunnum ◽

Joseph Cook

Keyword(s):

Ct Scans ◽

X Ray

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The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT

Science Progress ◽

10.1177/00368504211016204 ◽

2021 ◽

Vol 104 (3) ◽

pp. 003685042110162

Author(s):

Fengxia Zeng ◽

Yong Cai ◽

Yi Guo ◽

Weiguo Chen ◽

Min Lin ◽

...

Keyword(s):

Clinical Symptoms ◽

Diagnostic Value ◽

Rapid Identification ◽

Chest Ct ◽

Ct Scans ◽

Ground Glass ◽

X Ray ◽

Chest X Ray ◽

Laboratory Examinations ◽

Sensitivity Specificity

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients ( p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs ( p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

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Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

10.21203/rs.3.rs-20795/v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Dipayan Das ◽

KC Santosh ◽

Umapada Pal

Keyword(s):

Neural Network ◽

World Wide ◽

Imaging Techniques ◽

Imaging Systems ◽

Ct Scans ◽

X Rays ◽

X Ray ◽

X Ray Imaging ◽

Proposed Model ◽

Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

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