Relevant bibliographies by topics / Cone-beam

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cone-beam'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Cone-beam"

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Zeng, Gengsheng L. "Revisit of combined parallel-beam/cone-beam or fan-beam/cone-beam imaging." Medical Physics 40, no. 10 (September 10, 2013): 100701. http://dx.doi.org/10.1118/1.4820373.

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Radia, Ria, Judith Jones, and Jimmy Makdissi. "Cone beam specificity." Dental Update 49, no. 2 (February 2, 2022): 174–75. http://dx.doi.org/10.12968/denu.2022.49.2.174a.

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Gutmann, James L. "CONE-BEAM TECHNOLOGY." Journal of the American Dental Association 142, no. 3 (March 2011): 244–46. http://dx.doi.org/10.14219/jada.archive.2011.0150.

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opent, Bassam. "CONE BEAM CT." Tandartspraktijk 31, no. 9 (September 2010): 20–23. http://dx.doi.org/10.1007/s12496-010-0211-3.

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Frongia, Gianluigi, Maria Grazia Piancino, and Pietro Bracco. "Cone-Beam Computed Tomography." Journal of Craniofacial Surgery 23, no. 4 (July 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 (August 2015): 367. http://dx.doi.org/10.1097/id.0000000000000297.

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Manzke, Robert. "Cardiac cone-beam CT." Medical Physics 32, no. 10 (September 29, 2005): 3227. http://dx.doi.org/10.1118/1.2040707.

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Nasseh, Ibrahim, and Wisam Al-Rawi. "Cone Beam Computed Tomography." Dental Clinics of North America 62, no. 3 (July 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, Prashant Pujari, Apurva Chitalia, Sangeeta Prasad, and Suryansh Dilliwal. "Cone beam computed tomography." international journal of stomatology & occlusion medicine 8, no. 1 (March 2015): 1–7. http://dx.doi.org/10.1007/s12548-015-0121-y.

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Katsevich, Alexander. "Cone Beam Local Tomography." SIAM Journal on Applied Mathematics 59, no. 6 (January 1999): 2224–46. http://dx.doi.org/10.1137/s0036139998336043.

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Dissertations / Theses on the topic "Cone-beam"

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Almada, Levi Rafael Santos. "Cone Beam em endodontia." Master's thesis, [s.n.], 2011. http://hdl.handle.net/10284/2590.

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Trabalho apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária
A imagiologia como uma área de interesse à qual se recorre frequentemente em Medicina Dentária, tem vindo a evoluir muito nos últimos anos apresentando novas tecnologias. A Tomografia Computorizada de Feixe Cónico (CBCT) é uma dessas tecnologias mais recentes, que possibilita a visualização de imagens em três dimensões das estruturas dentárias e das estruturas ósseas adjacentes. Situações como identificação de canais radiculares “anormais” em quantidade e morfologia, detecção de fracturas radiculares verticais, avaliação do processo de recuperação pós tratamento, poderão ser melhor e mais facilmente identificadas e tratadas com esta nova opção imagiológica. O objectivo deste trabalho é analisar o uso da CBCT em Endodontia. Para isso recorreu-se a uma pesquisa na base de dados online PubMed e Scielo, limitando a pesquisa a publicações feitas após o ano 2008 inclusivé, de onde resultou a selecção de 42 artigos. Com a realização deste trabalho foi possível concluir que o uso CBCT em Endodontia é de facto uma mais-valia, constituindo uma excelente ferramenta de diagnóstico. Imaging, as an area of interest which is frequently used by dentistry, has evolved in recent years featuring new technologies. Cone Beam Computed Tomography (CBCT) is one of these newer technologies, which enables the visualization of three-dimensional images of dental structures and adjacent bone tissue. Situations as identification of abnormal number and morphology of root canals, detection of vertical root fractures, evaluation of healing process after treatment, may be better and more easily identified and dealt with this new imaging option. The aim of this study, is to analyze the use of CBCT in Endodontics. Online search was performed on PubMed and Scielo database, limiting the search to publications made after the year 2008 inclusive. This resulted in the selection of 42 articles. With this study, it was concluded that the use of CBCT in Endodontics is indeed a great asset, making it an excellent diagnosis tool.
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Yang, Xiaochun 1971. "Geometry of cone-beam reconstruction." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8338.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.
Includes bibliographical references (p. 89-91).
Geometry is the synthetic tool we use to unify all existing analytical cone-beam reconstruction methods. These reconstructions are based on formulae derived by Tuy [Tuy, 1983], Smith [Smith, 1985] and Grangeat [Grangeat, 1991] which explicitly link the cone-beam data to some intermediate functions in the Radon transform domain. However, the essential step towards final reconstruction, that is, differential-backprojection, has not yet achieved desired efficiency. A new inversion formula is obtained directly from the 3D Radon inverse [Radon, 1917, Helgason, 1999]. It incorporates the cone-beam scanning geometry and allows the theoretical work mentioned above to be reduced to exact and frugal implementations. Extensions can be easily carried out to 2D fan-beam reconstruction as well as other scanning modalities such as parallel scans by allowing more abstract geometric description on the embedding subspace of the Radon manifold. The new approach provides a canonical inverse procedure for computerized tomography in general, with applications ranging from diagnostic medical imaging to industrial testing, such as X-ray CT, Emission CT, Ultrasound CT, etc. It also suggests a principled frame for approaching other 3D reconstruction problems related to the Radon transform. The idea is simple: as was spelled out by Helgason on the opening page of his book, The Radon Transform [Helgason, 1999] - a remarkable duality characterizes the Radon transform and its inverse. Our study shows that the dual space, the so-called Radon space, can be geometrically decomposed according to the specified scanning modality.
(cont.) In cone-beam X-ray reconstruction, for example, each cone-beam projection is seen as a 2D projective subspace embedded in the Radon manifold. Besides the duality in the space relation, the symbiosis played between algebra and geometry, integration and differentiation is another striking feature in the tomographic reconstruction. Simply put, * Geometry and algebra: the two play fundamentally different roles during the inverse. Algebraic transforms carry cone-beam data into the Radon domain, whereas, the geometric decomposition of the dual space determines how the differential-backprojection operator should be systematically performed. The reason that different algorithms in cone-beam X-ray reconstruction share structural similarity is that the dual space decomposition is intrinsic to the specified scanning geometry. The differences in the algorithms lie in the appearance of algebra on the projection submanifold. The algebraic transforms initiate diverse reconstruction methods varying in terms of computational cost and stability. Equipped with this viewpoint, we are able to simplify mathematical analysis and develop algorithms that are easy to implement. Integration and differentiation: forward projection is the integral along straight lines (or planes) in the Euclidean space. During the reconstruction, differentiation is performed over the parallel planes in the projective Radon space, a manifold with clear differential structure. It is important to learn about this differential structure to ensure that correct differentiation can be carried out with respect to the parameters governing the scanning process during the reconstruction ...
by Xiaochun Yang.
Ph.D.
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Turbell, Henrik. "Cone-Beam Reconstruction Using Filtered Backprojection." Doctoral thesis, Linköping : Univ, 2001. http://www.bibl.liu.se/liupubl/disp/disp2001/tek672s.pdf.

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Rathore, Sonali A. Tyndall Donald A. "Cone beam CT in occlusal caries research." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2866.

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Thesis (M.S.)--University of North Carolina at Chapel Hill, 2009.
Title from electronic title page (viewed Jun. 4, 2010). "... in partial fulfillment of the requirements for the degree of Master of Science in the department of Oral and Maxillofacial Radiology." Discipline: Oral and Maxillofacial Radiology; Department/School: Dentistry.
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Allareddy, Veeratrishul. "Incidental findings on cone beam computed tomography." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/457.

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Watson, Peter. "Scatter artifact correction in cone-beam CT images." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117080.

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Cone-beam computed tomography (CBCT) images suffer from poor image quality, in a large part due to scattered x-rays. In this work, a fast and accurate Monte Carlo based scatter correction algorithm was implemented on real CBCT data. A fast Monte Carlo simulation developed in the EGSnrc framework was used to transport photons through an uncorrected CBCT scan. From the simulation output, the contribution from both primary and scattered photons for each projection image was estimated. Using these estimates, a subtractive scatter correction was performed on the CBCT projection data. Implementation of the scatter correction algorithm on CBCT phantom scans was shown to help mitigate scatter-induced artifacts, such as cupping and streaking. The scatter corrected images were also shown to have improved accuracy in reconstructed attenuation coefficient values. These results suggest that the proposed scatter correction algorithm is successful in improving image quality in real CBCT images, and are promising results towards the reliable use of CBCT images in adaptive radiotherapy.
Les images de tomodensitométrie à faisceau conique (CBCT) souffrent d'une qualité d'image inférieure en partie due aux rayonnement diffusés. Dans cet ouvrage, un algorithme Monte Carlo rapide et précis fut appliqué sur des images CBCT cliniques. En utilisant un logiciel de transport de particules à base Monte Carlo pour transporter des photons dans un CBCT où les données n'ont pas été corrigés, la contribution des photons diffusés primaires et secondaires pour chaque image fut estimée. En utilisant cet estimé, une correction fut apportée sur les données du CBCT. La méthode de correction CBCT a démontré sa capacité de mitiger les artéfacts introduient par la diffusion des photons. Les images corrigées ont montré une plus grande précision pour la reconstruction des coéfficients d'atténuation. Ces résultats suggèrent que la méthode proposée pour corriger des images CBCT fut un succès pour l'amélioration de la qualité d'images CBCT réelles, et insinuent une utilisation able des images CBCT en radiothérapie adaptative.
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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 smoothness in the prior. We use estimates of the true projection images (generated using Markov chain Monte Carlo, MCMC) in a conventional 3D reconstruction. The results are visually superior to those obtained using a frequency-domain smoothing filter. In our second analysis, we model the markers as they appear in the projection images. We restrict our model to regions of interest generated using morphological analysis. We combine the information from many projection images to generate an accurate estimate of the marker locations in 3D space. This produces accurate estimates of marker location, but no accurate measure of uncertainty. Our third analysis uses a template model for the markers in 3D space, with a separate model for the patient tissues. In phantom experiments, we obtain accurate estimates of the tissue properties and marker locations. For practical computational reasons, we can only analyse a small volume of the patient. Artefacts in the reconstruction used to determine the tissue properties outside the volume of interest prevent the successful estimation of both the tissue properties and marker locations in patients, but we accurately estimate the marker locations alone, with estimates of uncertainty. Additionally, we process the projection images, removing the markers. These processed images can be used to generate reconstructions with fewer artefacts.
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Vilches, Freixas Gloria. "Dual-energy cone-beam CT for proton therapy." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI099/document.

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La proton thérapie est une modalité de traitement du cancer qu’utilise des faisceaux de protons. Les systèmes de planification de traitement actuels se basent sur une image de l’anatomie du patient acquise par tomodensitométrie. Le pouvoir d’arrêt des protons relatif à l’eau (Stopping Power Ratio en Anglais, SPR) est déterminé à partir des unités Hounsfield (Hounsfield Units en Anglais, HU) pour calculer la dose absorbée au patient. Les protons sont plus vulnérables que les photons aux modifications du SPR du tissu dans la direction du faisceau dues au mouvement, désalignement ou changements anatomiques. De plus, les inexactitudes survenues de la CT de planification et intrinsèques à la conversion HU-SPR contribuent énormément à l’incertitude de la portée des protons. Dans la pratique clinique, au volume de traitement s’ajoutent des marges de sécurité pour tenir en compte ces incertitudes en détriment de perdre la capacité d’épargner les tissus autour de la tumeur. L’usage de l’imagerie bi-énergie en proton thérapie a été proposé pour la première fois en 2009 pour mieux estimer le SPR du patient par rapport à l’imagerie mono-énergie. Le but de cette thèse est d’étudier la potentielle amélioration de l’estimation du SPR des protons en utilisant l’imagerie bi-énergie, pour ainsi réduire l’incertitude dans la prédiction de la portée des protons dans le patient. Cette thèse est appliquée à un nouveau système d’imagerie, l’Imaging Ring (IR), un scanner de tomodensitométrie conique (Cone-Beam CT en Anglais, CBCT) développé pour la radiothérapie guidée par l’image. L’IR est équipé d’une source de rayons X avec un système d’alternance rapide du voltage, synchronisé avec une roue contenant des filtres de différents matériaux que permet des acquisitions CBCT multi-énergie. La première contribution est une méthode pour calibrer les modèles de source et la réponse du détecteur pour être utilisés en simulations d’imagerie X. Deuxièmement, les recherches ont évalué les facteurs que peuvent avoir un impact sur les résultats du procès de décomposition bi-énergie, dès paramètres d’acquisition au post-traitement. Les deux domaines, image et basée en la projection, ont été minutieusement étudiés, avec un spéciale accent aux approches basés en la projection. Deux nouvelles bases de décomposition ont été proposées pour estimer le SPR, sans avoir besoin d’une variable intermédiaire comme le nombre atomique effectif. La dernière partie propose une estimation du SPR des fantômes de caractérisation tissulaire et d’un fantôme anthropomorphique à partir d’acquisitions avec l’IR. Il a été implémentée une correction du diffusé, et il a été proposée une routine pour interpoler linéairement les sinogrammes de basse et haute énergie des acquisitions bi-énergie pour pouvoir réaliser des décompositions en matériaux avec données réelles. Les valeurs réconstruits du SPR ont été comparées aux valeurs du SPR expérimentales déterminés avec un faisceau d’ions de carbone
Proton therapy is a promising radiation treatment modality that uses proton beams to treat cancer. Current treatment planning systems rely on an X-ray computed tomography (CT) image of the patient's anatomy to design the treatment plan. The proton stopping-power ratio relative to water (SPR) is derived from CT numbers (HU) to compute the absorbed dose in the patient. Protons are more vulnerable than photons to changes in tissue SPR in the beam direction caused by movement, misalignment or anatomical changes. In addition, inaccuracies arising from the planning CT and intrinsic to the HU-SPR conversion greatly contribute to the proton range uncertainty. In clinical practice, safety margins are added to the treatment volume to account for these uncertainties at the expense of losing organ-sparing capabilities. The use of dual-energy (DE) in proton therapy was first suggested in 2009 to better estimate the SPR with respect to single-energy X-ray imaging. The aim of this thesis work is to investigate the potential improvement in determining proton SPR using DE to reduce the uncertainty in predicting the proton range in the patient. This PhD work is applied to a new imaging device, the Imaging Ring (IR), which is a cone-beam CT (CBCT) scanner developed for image-guided radiotherapy (IGRT). The IR is equipped with a fast kV switching X-ray source, synchronized with a filter wheel, allowing for multi-energy CBCT imaging. The first contribution of this thesis is a method to calibrate a model for the X-ray source and the detector response to be used in X-ray image simulations. It has been validated experimentally on three CBCT scanners. Secondly, the investigations have evaluated the factors that have an impact on the outcome of the DE decomposition process, from the acquisition parameters to the post-processing. Both image- and projection-based decomposition domains have been thoroughly investigated, with special emphasis on projection-based approaches. Two novel DE decomposition bases have been proposed to estimate proton SPRs, without the need for an intermediate variable such as the effective atomic number. The last part of the thesis proposes an estimation of proton SPR maps of tissue characterization and anthropomorphic phantoms through DE-CBCT acquisitions with the IR. A correction for X-ray scattering has been implemented off-line, and a routine to linearly interpolate low-energy and high-energy sinograms from sequential and fast-switching DE acquisitions has been proposed to perform DE material decomposition in the projection domain with real data. DECT-derived SPR values have been compared with experimentally-determined SPR values in a carbon-ion beam
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Ashfaq, Awais. "Segmentation of Cone Beam CT in Stereotactic Radiosurgery." Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193107.

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C-arm Cone Beam CT (CBCT) systems – due to compact size, flexible geometry and low radiation exposure – inaugurated the era of on-board 3D image guidance in therapeutic and surgical procedures. Leksell Gamma Knife Icon by Elekta introduced an integrated CBCT system to determine patient position prior to surgical session, thus advancing to a paradigm shift in facilitating frameless stereotactic radiosurgeries. While CBCT offers a quick imaging facility with high spatial accuracy, the quantitative values tend to be distorted due to various physics based artifacts such as scatter, beam hardening and cone beam effect. Several 3D reconstruction algorithms targeting these artifacts involve an accurate and fast segmentation of craniofacial CBCT images into air, tissue and bone. The objective of the thesis is to investigate the performance of deep learning based convolutional neural networks (CNN) in relation to conventional image processing and machine learning algorithms in segmenting CBCT images. CBCT data for training and testing procedures was provided by Elekta. A framework of segmentation algorithms including multilevel automatic thresholding, fuzzy clustering, multilayer perceptron and CNN is developed and tested against pre-defined evaluation metrics carrying pixel-wise prediction accuracy, statistical tests and execution times among others. CNN has proven its ability to outperform other segmentation algorithms throughout the evaluation metrics except for execution times. Mean segmentation error for CNN is found to be 0.4% with a standard deviation of 0.07%, followed by fuzzy clustering with mean segmentation error of 0.8% and a standard deviation of 0.12%. CNN based segmentation takes 500s compared to multilevel thresholding which requires ~1s on similar sized CBCT image. The present work demonstrates the ability of CNN in handling artifacts and noise in CBCT images and maintaining a high semantic segmentation performance. However, further efforts targeting CNN execution speed are required to utilize the segmentation framework within real-time 3D reconstruction algorithms.
C-arm Cone Beam CT (CBCT) system har tack vare sitt kompakta format, flexibla geometri och låga strålningsdos startat en era av inbyggda 3D bildtagningssystem för styrning av terapeutiska och kirurgiska ingripanden. Elektas Leksell Gamma Knife Icon introducerade ett integrerat CBCT-system för att bestämma patientens position för operationer och på så sätt gå in i en paradigm av ramlös stereotaktisk strålkirurgi. Även om CBCT erbjuder snabb bildtagning med hög spatiel noggrannhet så tenderar de kvantitativa värdena att störas av olika artefakter som spridning, beam hardening och cone beam effekten. Ett flertal 3D rekonstruktionsalgorithmer som försöker reducera dessa artefakter kräver en noggrann och snabb segmentering av kraniofaciala CBCT-bilder i luft, mjukvävnad och ben. Målet med den här avhandlingen är att undersöka hur djupa neurala nätverk baserade på faltning (convolutional neural networks, CNN) presterar i jämförelse med konventionella bildbehandlings- och maskininlärningalgorithmer för segmentering av CBCT-bilder. CBCT-data för träning och testning tillhandahölls av Elekta. Ett ramverk för segmenteringsalgorithmer inklusive flernivåströskling (multilevel automatic thresholding), suddig klustring (fuzzy clustering), flerlagersperceptroner (multilayer perceptron) och CNN utvecklas och testas mot fördefinerade utvärderingskriterier som pixelvis noggrannhet, statistiska tester och körtid. CNN presterade bäst i alla metriker förutom körtid. Det genomsnittliga segmenteringsfelet för CNN var 0.4% med en standardavvikelse på 0.07%, följt av suddig klustring med ett medelfel på 0.8% och en standardavvikelse på 0.12%. CNN kräver 500 sekunder jämfört med ungefär 1 sekund för den snabbaste algorithmen, flernivåströskling på lika stora CBCT-volymer. Arbetet visar CNNs förmåga att handera artefakter och brus i CBCT-bilder och bibehålla en högkvalitativ semantisk segmentering. Vidare arbete behövs dock för att förbättra presetandan hos algorithmen för att metoden ska vara applicerbar i realtidsrekonstruktionsalgorithmer.
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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.
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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Books on the topic "Cone-beam"

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Sarment, David, ed. Cone Beam Computed Tomography. Chichester, UK: 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. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-62061-9.

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Caruso, Pietro, Enzo Silvestri, and Luca Maria Sconfienza, eds. Cone Beam CT and 3D imaging. Milano: Springer Milan, 2014. http://dx.doi.org/10.1007/978-88-470-5319-9.

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

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Kau, Chung H., Kenneth Abramovitch, Sherif Galal Kamel, and Marko Bozic. Cone Beam CT of the Head and Neck. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12704-5.

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Computed tomography: From photon statistics to modern cone-beam CT. Berlin: Springer, 2008.

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Color atlas of cone beam volumetric imaging for dental applications. Hanover Park, IL: Quintessence Pub., 2008.

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

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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. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31949-6.

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Scherl, Holger. Evaluation of State-of-the-Art Hardware Architectures for Fast Cone-Beam CT Reconstruction. Wiesbaden: Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-8259-2.

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Book chapters on the topic "Cone-beam"

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Patel, Nisha R., Michael L. Wong, Anthony E. Dragun, Stephan Mose, Bernadine R. Donahue, Jay S. Cooper, Filip T. Troicki, et al. "Megavoltage Cone Beam." In Encyclopedia of Radiation Oncology, 491. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_1177.

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Patel, Nisha R., Michael L. Wong, Anthony E. Dragun, Stephan Mose, Bernadine R. Donahue, Jay S. Cooper, Filip T. Troicki, et al. "MV Cone Beam." In Encyclopedia of Radiation Oncology, 521. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_1186.

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Zeichner, Samuel J. "Cone Beam CT." In Digital Technologies in Craniomaxillofacial Surgery, 47–53. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-1532-3_3.

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Jacobson, Matthew W. "Technology and Principles of Cone Beam Computed Tomography." In Cone Beam Computed Tomography, 1–24. Chichester, UK: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch1.

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Levin, Martin D. "Endodontics Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography, 211–47. Chichester, UK: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch10.

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Vandenberghe, Bart, and David Sarment. "Periodontal Disease Diagnosis Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography, 249–69. Chichester, UK: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch11.

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Mallya, Sanjay M., and Stuart C. White. "The Nature of Ionizing Radiation and the Risks from Maxillofacial Cone Beam Computed Tomography." In Cone Beam Computed Tomography, 25–41. Chichester, UK: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch2.

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Brooks, Sharon L. "Diagnosis of Jaw Pathologies Using Cone Beam Computed Tomography." In Cone Beam Computed Tomography, 43–64. Chichester, UK: 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, 65–90. Chichester, UK: 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, 91–107. Chichester, UK: John Wiley & Sons, Inc, 2013. http://dx.doi.org/10.1002/9781118769027.ch5.

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Conference papers on the topic "Cone-beam"

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Hu, Hui, Robert A. Kruger, and Grant T. Gullberg. "Quantitative Cone-Beam Reconstruction." In 1989 Medical Imaging, edited by Samuel J. Dwyer III, R. Gilbert Jost, and Roger H. Schneider. SPIE, 1989. http://dx.doi.org/10.1117/12.953290.

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Kachelriess, Marc, Michael Knaup, and Olivier Bockenbach. "Hyperfast Perspective Cone--Beam Backprojection." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.354221.

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Li, Ming, Nadine Abi-Jaoudeh, Ankur Kapoor, Samuel Kadoury, Sheng Xu, Niels Noordhoek, Alessandro Radaelli, Bart Carelsen, and Bradford J. Wood. "Towards cone-beam CT thermometry." In SPIE Medical Imaging, edited by David R. Holmes and Ziv R. Yaniv. SPIE, 2013. http://dx.doi.org/10.1117/12.2006851.

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Yu, Hengyong, Yangbo Ye, and Ge Wang. "Practical cone-beam lambda tomography." In SPIE Optics + Photonics, edited by Ulrich Bonse. SPIE, 2006. http://dx.doi.org/10.1117/12.682202.

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Ye, Yangbo, Hengyong Yu, and Ge Wang. "Skew cone beam lambda tomography." In SPIE Optics + Photonics, edited by Ulrich Bonse. SPIE, 2006. http://dx.doi.org/10.1117/12.683236.

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Schomberg, Hermann. "A Proposed Cone Beam Version of Electron Beam CT." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.356536.

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Desbat, Laurent, Rolf Clackdoyle, Louise Grezes-Besset, Catherine Mennessier, and Ivan Bricault. "Cone-Beam Imaging of Delta Functions." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.356473.

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Nielsen, Tim, Robert Manzke, Thomas Koehler, Michael Grass, and Roland Proksa. "Iterative cardiac cone-beam CT reconstruction." In Medical Imaging 2004, edited by J. Michael Fitzpatrick and Milan Sonka. SPIE, 2004. http://dx.doi.org/10.1117/12.534847.

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Trousset, Yves L., Didier M. Saint-Felix, Anne Rougee, and Christine Chardenon. "Multiscale cone-beam x-ray reconstruction." In Medical Imaging '90, Newport Beach, 4-9 Feb 90, edited by Roger H. Schneider. SPIE, 1990. http://dx.doi.org/10.1117/12.18800.

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Hsieh, Jiang, and Xiangyang Tang. "Tilted cone beam VCT reconstruction algorithm." In Medical Imaging, edited by Michael J. Flynn. SPIE, 2005. http://dx.doi.org/10.1117/12.594700.

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Reports on the topic "Cone-beam"

1

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

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Remeijer, P., K. Deurloo, M. Eenink, K. Geleijns, J. Hermans, H. Van Herpt, M. Hol, et al. NCS Report 32: Quality assurance of cone-beam CT. Delft: NCS, March 2019. http://dx.doi.org/10.25030/ncs-032.

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Chao, Ming. Automated Patient Positioning Guided by Cone-Beam CT for Prostate Radiotherapy. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada486477.

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Chao, Ming. Automated Patient Positioning Guided by Cone-Beam CT for Prostate Radiotherapy. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada495707.

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Gullberg, Grant T., Qiu Huang, Jiangsheng You, and Gengsheng L. Zeng. Exact Reconstruction From Uniformly Attenuated Helical Cone-Beam Projections in SPECT. Office of Scientific and Technical Information (OSTI), December 2008. http://dx.doi.org/10.2172/945943.

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Li, Tianfang. Automated Patient Positioning Guided by Cone-Beam CT for Prostate Radiotherapy. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada466162.

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

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Klasky, Marc, Anish Lahiri, Saiprasad Ravishankar, Erik Skau, and Michelle Espy. Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1699436.

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

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Han, Xiao, and Betty Diamond. Development of Prior Image-based, High-Quality, Low-Dose Kilovoltage Cone Beam CT for Use in Adaptive Radiotherapy of Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada563072.

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