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Relevant bibliographies by topics / Cone-Beam Computed Tomography

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Academic literature on the topic 'Cone-Beam Computed Tomography'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Cone-Beam Computed Tomography"

1

Frongia, Gianluigi, Maria Grazia Piancino, and Pietro Bracco. "Cone-Beam Computed Tomography." Journal of Craniofacial Surgery 23, no. 4 (2012): 1038–43. http://dx.doi.org/10.1097/scs.0b013e318252d5e1.

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Perel, Morton L. "Cone-Beam Computed Tomography." Implant Dentistry 24, no. 4 (2015): 367. http://dx.doi.org/10.1097/id.0000000000000297.

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Nasseh, Ibrahim, and Wisam Al-Rawi. "Cone Beam Computed Tomography." Dental Clinics of North America 62, no. 3 (2018): 361–91. http://dx.doi.org/10.1016/j.cden.2018.03.002.

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Pereira, Ulrika Diana, Deepak Kalia, Prerna Raje Batham, et al. "Cone beam computed tomography." international journal of stomatology & occlusion medicine 8, no. 1 (2015): 1–7. http://dx.doi.org/10.1007/s12548-015-0121-y.

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Vyzhenko, Ye Ye. "CEPHALOMETRIC ANALYSIS BASED ON CONE-BEAM COMPUTER TOMOGRAPHY (LITERATURE REVIEW)." Ukrainian Dental Almanac, no. 4 (December 25, 2023): 60–66. http://dx.doi.org/10.31718/2409-0255.4.2023.10.

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Conducting a cephalometric analysis is an integral component in the diagnosis of malocclusion. Inaccuracies in the identification of landmarks on two-dimensional images can lead to measurement errors. Threedimensional analysis based on cone-beam computed tomography expands diagnostic possibilities in clinical practice. In the electronic database of PubMed for the period from 2017 to September 1, 2023, an analysis of the scientific literature was carried out using the cephalometric analysis and cone-beam computed tomography, which describe the methods and statistically proven effectiveness of c

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Wang, Wei, Ting Yu, Min Xu, Qian Shao, Yingjie Zhang, and Jianbin Li. "Setup Error Assessment and Correction in Planar kV Image- Versus Cone Beam CT Image-Guided Radiation Therapy: A Clinical Study of Early Breast Cancer Treated With External Beam Partial Breast Irradiation." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381985384. http://dx.doi.org/10.1177/1533033819853847.

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Objective: To compare differences in setup error assessment and correction between planar kilovolt images and cone beam computed tomography images for external beam partial breast irradiation during free breathing. Methods: Nineteen patients who received external beam partial breast irradiation after breast-conserving surgery were recruited. Interfraction setup error was acquired using planar kilovolt images and cone beam computed tomography. After online setup correction, the residual error was calculated, and the setup error was compared. The residual error and setup margin were quantified f

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Smorthit, Kelly, Jonathan Sandler, and Catherine Brierley. "Tips for viewing and interpreting cone beam computed tomography radiographs in orthodontics." Orthodontic Update 17, no. 1 (2024): 33–35. http://dx.doi.org/10.12968/ortu.2024.17.1.33.

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Cone beam computed tomographic imaging has increasingly become an important adjunct to our diagnostic toolbox in orthodontics. This article provides some practical tips to clinicians for the viewing and interpretation of cone beam computed tomography in orthodontics, particularly in relation to root resorption. CPD/Clinical relevance: This article informs the orthodontic team on the applications of cone beam computed tomography and provides practice tips for viewing and interpreting images in practice, with particular regard to root resorption.

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Remulla, Srujana Daniella, Jyothirmai Koneru, Sudhakara Reddy, Ramesh Tatapudi, Geetanjali Darna, and Naga Manikanta Mohan Prathipati. "Prediction of nerve damage by comparing periapical radiographic signs of impacted mandibular third molars in close proximity to inferior alveolar nerve with their true tomographic relationship – An observational study." IP International Journal of Maxillofacial Imaging 7, no. 3 (2021): 125–30. http://dx.doi.org/10.18231/j.ijmi.2021.023.

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The purpose of the study was to correlate the accuracy of Roods and Shehab signs in an intraoral periapical radiograph (IOPAR) with Cone-beam computed tomography (CBCT) findings to indicate Cone-beam computed tomography only in high-risk conditions.70 impacted mandibular third molar teeth in 58 patients above 18 years with intraoral periapical radiographs presenting with one or more root and canal signs of Rood and Shehab criteria were included in the study. Winter's classification was recorded, and the patients were exposed to a Cone-beam computed tomographic scan. True canal – tooth relation

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Izzetti, Rossana, Raffaele Gaeta, Davide Caramella, and Valentina Giuffra. "Cone-Beam Computed Tomography vs. Multi-Slice Computed Tomography in paleoimaging: where we stand." HOMO 71, no. 1 (2020): 63–72. http://dx.doi.org/10.1127/homo/2020/1063.

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Alam, Mir Ramiz, Salma Khiti, Anirban Das, and Sanjay Prasad. "A cone beam computed tomography and oral-maxillofacial pathology: A review." Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology 10, no. 4 (2024): 228–30. https://doi.org/10.18231/j.jooo.2024.045.

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The use of Cone Beam Computed Tomography has significantly increased in the last decade. The goal is to provide images of oro-facial problems in all three planes, with additional features of three-dimensional reconstruction. Compared to Orthopantomograph, Cone Beam Computed Tomography offers greater accuracy in measurement and lesser image distortion. It can be used as an important tool for assessing the cortical perforation of aggressive benign cysts or tumors. Cone Beam Computed Tomography sialography can serve as a supplementary noninvasive diagnostic tool for imaging the intraglandular duc

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Dissertations / Theses on the topic "Cone-Beam Computed Tomography"

1

Allareddy, Veeratrishul. "Incidental findings on cone beam computed tomography." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/457.

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Doshi, Susan. "Statistical image analysis in cone-beam computed tomography." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619218.

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Cone-beam computed tomography (CBCT) is used to verify the patient’s position prior to commencing radiotherapy treatment. Soft tissues such as the prostate are hard to distinguish, and so gold markers may be implanted. These markers cause artefacts in the 3D reconstruction. In this thesis, we apply statistical image analysis techniques to CBCT data, with two purposes: we estimate the marker locations (with an assessment of uncertainty), and create reconstructions with fewer artefacts. In our first analysis, we define a Bayesian statistical model for the projection data, encouraging local smoot

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Balasundaram, Ashok Mol André. "Cone beam computed tomography imaging of periodontal bone." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,2063.

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Thesis (M.S.)--University of North Carolina at Chapel Hill, 2008.<br>Title from electronic title page (viewed Feb. 17, 2009). "... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Diagnostic Sciences and General Dentistry, School of Dentistry." Discipline: Diagnostic Sciences and General Dentistry; Department/School: Dentistry.

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Mason, Jonathan Hugh. "Quantitative cone-beam computed tomography reconstruction for radiotherapy planning." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33193.

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Radiotherapy planning involves the calculation of dose deposition throughout the patient, based upon quantitative electron density images from computed tomography (CT) scans taken before treatment. Cone beam CT (CBCT), consisting of a point source and flat panel detector, is often built onto radiotherapy delivery machines and used during a treatment session to ensure alignment of the patient to the plan. If the plan could be recalculated throughout the course of treatment, then margins of uncertainty and toxicity to healthy tissues could be reduced. CBCT reconstructions are normally too poor t

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Amor, Zaineb. "Bone segmentation and extrapolation in Cone-Beam Computed Tomography." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279802.

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This work was done within the French R&D center of GE Medical Systems and focused on two main tasks: skull bone segmentation on 3D Cone-Beam Computed Tomography (CBCT) data and skull volumetric shape extrapolation on 3D CBCT data using deep learning approaches. The motivation behind the first task is that it would allow interventional radiologists to visualize only the vessels directly without adding workflow to their procedures and exposing the patients to extra radiations. The motivation behind the second task is that it would help understand and eventually correct some artifacts related

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Adler, Jonas. "GPU Monte Carlo scatter calculations for Cone Beam Computed Tomography." Thesis, KTH, Numerisk analys, NA, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140221.

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A GPU Monte Carlo code for x-ray photon transport has been implemented and extensively tested. The code is intended for scatter compensation of cone beam computed tomography images. The code was tested to agree with other well known codes within 5% for a set of simple scenarios. The scatter compensation was also tested using an artificial head phantom. The errors in the reconstructed Hounsfield values were reduced by approximately 70%. Several variance reduction methods have been tested, although most were found infeasible on GPUs. The code is nonetheless fast, and can simulate approximately 3

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Patel, Shanon. "The clinical applications of cone beam computed tomography in endodontics." Thesis, King's College London (University of London), 2012. https://kclpure.kcl.ac.uk/portal/en/theses/the-clinical-applications-of-cone-beam-computed-tomography-in-endodontics(28daff53-0941-4b36-b8ea-beeb7dc2a8e6).html.

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A series of 5 investigations assessed the application of cone beam computed tomography (CBCT) for the management of endodontic problems. Cone beam computed tomography improved the detection of the presence and absence of simulated periapical lesions in human dry mandibles. The overall sensitivity was 0.248 and 1.0 for periapical radiography and CBCT respectively. The receiver operating characteristics (ROC) area under the curve (AUC) values were 0.791 and 1.000 for intraoral radiography and CBCT, respectively. There was no improvement in the detection of artificially created vertical root frac

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Schöndube, Harald [Verfasser]. "Helical Cone-Beam Computed Tomography using the Differentiated Backprojection / Harald Schöndube." Aachen : Shaker, 2010. http://d-nb.info/1106838726/34.

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England, Gregory Miles. "Effect of Scanning Conditions on Cone Beam Computed Tomography Gray Value." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1458951153.

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Markwell, Timothy S. "MegaVoltage Cone Beam Computed Tomography with a standard medical linear accelerator." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/87438/1/Timothy_Markwell_Thesis.pdf.

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Accurate patient positioning is vital for improved clinical outcomes for cancer treatments using radiotherapy. This project has developed Mega Voltage Cone Beam CT using a standard medical linear accelerator to allow 3D imaging of the patient position at treatment time with no additional hardware required. Providing 3D imaging functionality at no further cost allows enhanced patient position verification on older linear accelerators and in developing countries where access to new technology is limited.

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Books on the topic "Cone-Beam Computed Tomography"

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Sarment, David, ed. Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.

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Scarfe, William C., and Christos Angelopoulos, eds. Maxillofacial Cone Beam Computed Tomography. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-62061-9.

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Gonzalez, Shawneen M., ed. Interpretation Basics of Cone Beam Computed Tomography. John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781119421177.

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Kapila, Sunil D., ed. Cone Beam Computed Tomography in Orthodontics: Indications, Insights, and Innovations. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118674888.

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Lofthag-Hansen, Sara. Cone beam computed tomography: Radiation dose and image quality assessments. University of Gothenburg, 2010.

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6

Richter, Martinus, Francois Lintz, Cesar de Cesar Netto, Alexej Barg, Arne Burssens, and Scott Ellis. Weight Bearing Cone Beam Computed Tomography (WBCT) in the Foot and Ankle. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31949-6.

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Shaw, Chris C. Cone Beam Computed Tomography. Taylor & Francis Group, 2014.

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Cone Beam Computed Tomography. Taylor & Francis Group, 2014.

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Shaw, Chris C. Cone Beam Computed Tomography. Taylor & Francis Group, 2014.

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Shaw, Chris C. Cone Beam Computed Tomography. Taylor & Francis Group, 2020.

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Book chapters on the topic "Cone-Beam Computed Tomography"

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Siewerdsen, Jeffrey H. "Cone-Beam CT Systems." In Computed Tomography. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26957-9_2.

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2

Larheim, Tore A. "Cone Beam Computed Tomography." In Maxillofacial Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53319-3_15.

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Guess, Garrett, Fouad Al-Malki, Meetu Kohli, Bekir Karabucak, and Samuel Kratchman. "Cone Beam Computed Tomography." In Microsurgery in Endodontics. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119412502.ch14.

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Levin, Martin D. "Endodontics Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch10.

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Sarment, David. "Implant Planning Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch7.

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Jacobson, Matthew W. "Technology and Principles of Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch1.

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Vandenberghe, Bart, and David Sarment. "Periodontal Disease Diagnosis Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch11.

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Brooks, Sharon L. "Diagnosis of Jaw Pathologies Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch3.

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Miracle, Aaron, and Christian Güldner. "Diagnosis of Sinus Pathologies Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch4.

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Cevidanes, Lucia H. S., Martin Styner, Beatriz Paniagua, and João Roberto Gonçalves. "Orthodontic and Orthognathic Planning Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography. John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch5.

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Conference papers on the topic "Cone-Beam Computed Tomography"

1

Yunker, Austin, Jason Luce, John C. Roeske, et al. "Noise2Inverse for 3D Low-Dose Cone-Beam Computed Tomography." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825036.

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Illescas, J., M. Perez, J. Fajardo, A. Lara, and F. Reyna. "AI-based Automatic Segmentation of Sinonasal Cavity in Cone-Beam Computed Tomography." In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE, 2024. https://doi.org/10.1109/sipaim62974.2024.10783618.

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Maass, C., M. Knaup, S. Sawall, and M. Kachelriess. "Simple ROI cone-beam computed tomography." In 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5874385.

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Brehm, Marcus, Timo Berkus, Markus Oehlhafen, Patrik Kunz, and Marc Kachelriess. "Motion-compensated 4D cone-beam computed tomography." In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (2011 NSS/MIC). IEEE, 2011. http://dx.doi.org/10.1109/nssmic.2011.6153758.

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Zheng, Jia, Dinghua Zhang, Kuidong Huang, and Yuanxi Sun. "Cone-Beam Computed Tomography Image Pretreatment and Segmentation." In 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018. http://dx.doi.org/10.1109/iscid.2018.00012.

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Conover, David, Ruola Ning, Scott Paoni, et al. "Cone beam computed tomography angiogenesis imaging: preliminary studies." In Medical Imaging, edited by Armando Manduca and Amir A. Amini. SPIE, 2006. http://dx.doi.org/10.1117/12.655655.

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Ingleby, Harry R., Idris A. Elbakri, Daniel W. Rickey, and Stephen Pistorius. "Analytical scatter estimation for cone-beam computed tomography." In SPIE Medical Imaging, edited by Ehsan Samei and Jiang Hsieh. SPIE, 2009. http://dx.doi.org/10.1117/12.813804.

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Gang, G. J., J. W. Stayman, S. Ouadah, T. Ehtiati, and J. H. Siewerdsen. "Task-driven imaging in cone-beam computed tomography." In SPIE Medical Imaging, edited by Christoph Hoeschen, Despina Kontos, and Thomas G. Flohr. SPIE, 2015. http://dx.doi.org/10.1117/12.2082169.

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Buliev, I. G., C. T. Badea, and N. Pallikarakis. "Respiratory displacement modelling in cone beam computed tomography." In Computers in Cardiology, 2003. IEEE, 2003. http://dx.doi.org/10.1109/cic.2003.1291231.

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"Simulated dental cone beam computed tomography using Timepix." In 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC). IEEE, 2013. http://dx.doi.org/10.1109/nssmic.2013.6829123.

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Reports on the topic "Cone-Beam Computed Tomography"

1

Azevedo, S., P. Rizo, and P. Grangeat. Region-of-interest cone-beam computed tomography. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/125412.

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Cho, Seungryong. Cone-Beam Computed Tomography for Image-Guided Radiation Therapy of Prostate Cancer. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada480130.

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Anderson, William M. Studying the Prevalence and Etiology of Class II Subdivision Malocclusion Utilizing Cone-Beam Computed Tomography. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ad1012894.

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Malinowski, Owen, Scott Riccardella, and Jason Van Velsor. PR-335-203810-R03 CT Fundamentals with Calibration and Reference Standards for Pipeline Anomaly Detection. Pipeline Research Council International, Inc. (PRCI), 2022. http://dx.doi.org/10.55274/r0012216.

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X-ray Computed Tomography (XRCT) was initially developed and utilized in the medical industry to image the internal structure of the human body. X-ray imaging was conceived and realized at the turn of the 20th century and subsequently, XRCT, was conceived in the middle of the 20th century and its development continues today. Near the end of the 20th century industrial cone beam XRCT for applications such as dimensional metrology branched off, including its use for identifying and dimensioning flaws. XRCT has been utilized successfully for three-dimensional imaging of flaws in the small panel c

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