Home | About us | Editorial board | Search | Ahead of print | Current issue | Archives | Submit article | Instructions| Reviewers

  Home Print this page Email this page Small font sizeDefault font sizeIncrease font size Users Online: 707    

Table of Contents   
Year : 2015  |  Volume : 5  |  Issue : 6  |  Page : 440-445
Artifacts: The downturn of CBCT image

1 Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India
2 Department of Orthodontica and Dentofacial Orthopedics, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India

Date of Web Publication26-Nov-2015

Correspondence Address:
Neha Dwivedi
MIG-B-2, Hitkarini Dental College, Dumna Road, Jabalpur. .. 482. 001, Madhya Pradesh,
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2231-0762.170523

Rights and Permissions


Cone-beam computed tomography (CBCT) has been accepted as a useful tool for diagnosis and treatment planning in dentistry. Despite a growing trend of CBCT in dentistry, it has some disadvantages like artifacts. Artifacts are discrepancies between the reconstructed visual image and the actual content of the subject which degrade the quality of CBCT images, making them diagnostically unusable. Additionally, structures that do not exist in the subject may appear within images. Such structures can occur because of patient motion, the image capture and reconstruction process. To optimize image quality, it is necessary to understand the types of artifacts. This article aims to throw light on the various types of artifacts associated with CBCT images.

Keywords: Aliasing artifacts, beam hardening, motion artifacts, scanner-related artifacts, stair step artifacts, zebra artifacts

How to cite this article:
Nagarajappa AK, Dwivedi N, Tiwari R. Artifacts: The downturn of CBCT image. J Int Soc Prevent Communit Dent 2015;5:440-5

How to cite this URL:
Nagarajappa AK, Dwivedi N, Tiwari R. Artifacts: The downturn of CBCT image. J Int Soc Prevent Communit Dent [serial online] 2015 [cited 2022 Jul 1];5:440-5. Available from: https://www.jispcd.org/text.asp?2015/5/6/440/170523

   Introduction Top

The first cone-beam computed tomography (CBCT) machine developed strictly for maxillofacial imaging was the NewTom-9000 (Quantitative Radiology, Verona, Italy). Since its development in 1998, there has been a rapid progression in the production of CBCT units manufactured for imaging the maxillofacial region. Like conventional CT used in medicine, CBCT provides a means for three-dimensional (3D) imaging. However, in dentistry, CBCT is designed for imaging the maxillofacial region and, therefore, can be applied to diagnostic imaging tasks specific to the field of dentistry. In addition, the radiation dose is lower in CBCT used in dentistry than in CT used in medicine.[1]

However, there are some drawbacks in using CBCT as an imaging technique. The presence of gray-level non-uniformities in CBCT contributes to artifact formation in reconstructed CBCT images. In CT, the term "artifact" refers to any systematic discrepancy between the CT numbers in the reconstructed image and the true attenuation coefficients of the object.[1] Artifacts are commonly encountered in clinical CT, and may obscure or simulate pathology. There are many different types of CT artifacts [Figure 1], including noise, beam hardening, scatter, pseudoenhancement, motion, cone beam, helical, ring, and metal artifacts.[2]
Figure 1: Classification of artifacts

Click here to view

   X-Ray Beam Artifacts Top

Beam hardening

Beam hardening is one of the most prominent sources of artifacts. An X-ray beam is composed of individual photons with a range of energies. As the beam passes through an object, it becomes "harder," i.e., its mean energy increases, because the lower-energy photons are absorbed more rapidly than the higher-energy photons.[3],[4] Highly absorbing materials such as metal [3],[5],[6] function as a filter positioned within the object. If the emitted spectrum contains more relatively lower-energetic rays than that recorded on the detector (i.e. the beam is hardened), a non-linear error (relatively too much energy recorded in the beam path behind highly absorbing materials) is induced in the recorded data. In the 3D reconstruction, the error is back projected into the volume, resulting in darks streaks.[3],[5],[7] Because the CBCT X-ray beam is heterochromatic and has lower mean kilovolt (peak) energy compared with conventional CT, this artifact is more pronounced on CBCT images. These can be reduced using iterative reconstruction. Two types of artifact can result from this effect: The so-called cupping artifacts and the appearance of dark bands or streaks between dense objects in the image [Figure 2].[1],[2],[6],[8] In clinical practice, it is advisable to reduce the field of view (FOV)[9] to avoid scanning regions susceptible to beam hardening (e.g., metallic restorations, dental implants), which can be achieved by collimation, modification of patient positioning, or separation of the dental arches.[6],[5] More recently, dental CBCT manufacturers have introduced artifact reduction technique algorithms within the reconstruction process (e.g., Scanora 3D; SOREDEX, Helsinki, Finland). These algorithms reduce image noise, metal and motion-related artifacts and require fewer projection images, and therefore may allow for a lower acquisition dose. However, they are computationally demanding and require increased reconstruction times.[10],[11],[12],[13],[14]
Figure 2: Beam hardening artifact adjacent to a silver point and metal artifact streaks from the metal coping

Click here to view

Manufacturers minimize beam hardening by using filtration, calibration correction, and beam hardening correction software.[15]


A flat piece of attenuating, usually metallic material is used to "pre-harden" the beam by filtering out the lower-energy components before it passes through the patient. An additional "bowtie" filter further hardens the edges of the beam, which will pass through the thinner parts of the patient.[15]

Calibration correction

Manufacturers calibrate their scanners using phantoms in a range of sizes. This allows the detectors to be calibrated with compensation tailored for the beam hardening effects of different parts of the patient.[15]

Beam hardening correction software

An iterative correction algorithm may be applied when images of bony regions are being reconstructed. This helps minimize blurring of the bone–soft tissue interface in brain scans and also reduces the appearance of dark bands in non-homogeneous cross sections.[15]

Avoidance of beam hardening by the operator

It is sometimes possible to avoid scanning bony regions, either by means of patient positioning or by tilting the gantry.[15]

Cupping artifact

The cupping effect artifact is demonstrated when a uniform cylindrical object is imaged. X-rays passing through the middle portion of a uniform cylindrical phantom are hardened more than those passing though the edges because they are passing though more material. As the beam becomes harder, the rate at which it is attenuated decreases. The gray levels decrease in value in the center of the aluminum cylinder owing to the increase in transmitted intensity to the detector from the presence of beam hardening and scatter radiation occurring during image acquisition. Therefore, the resultant attenuation profile differs from the ideal profile that would be obtained without beam hardening and displays a characteristic cupped shape artifact.[1],[15]

   Patient-Related Artifacts Top

Patient motion can cause misregistration of data, which appears as unsharpness [Figure 3] in the reconstructed image. This unsharpness can be minimized by using a head restraint and as short a scan time as possible.[2],[12]
Figure 3: Blurring and double cortices caused by motion artifact

Click here to view

Avoidance of metal artifacts by the operator

Patients are normally asked to take off removable metal objects such as jewelry before scanning commences. For non-removable items, such as dental fillings, prosthetic devices, and surgical clips, it is sometimes possible to use gantry angulation to exclude the metal inserts from scans of nearby anatomy. When it is impossible to scan the required anatomy without including metal objects, increasing technique, especially kilovoltage, may help penetrate some objects, and using thin sections will reduce the contribution due to partial volume artifact.[15]

Motion artifacts–misalignment artifacts

These two sources of error are closely related in that a misalignment of any of the three components (source, object, and detector) causes inconsistencies in the back projection process. Patient motion can cause misregistration artifacts within the image. If an object moves during the scanning process, the reconstruction does not account for that move since no information on the movement is integrated in the reconstruction process.[2],[12],[14]

Hence, the lines along which the back projection takes place do not correspond to the lines along which the attenuation had been recorded, simply because the object has moved during the acquisition. The smaller the voxel size (i.e., the higher the spatial resolution), the smaller the movement necessary to move the patient structures out of the "correct" voxels. Movement artifacts present as double contours.[2],[11],[12],[16],[17]

Misalignment of the source relative to the detector or the unit of the two of them relative to the stationary patient causes the same sort of inconsistencies as described above. This applies also for minute deviations, e.g., deviations from a truly planar circular source and detector trajectory. This results in poor overall image quality. Since the resolutions of the present CBCT are very high, ranging from 0.08 to 0.4 mm, even small motions can have a detrimental effect on image quality. To prevent these sorts of errors poses great challenges on the mechanical stability of the systems.[2],[12],[16],[17]

Avoidance of motion artifacts by the operator

The use of positioning aids is sufficient to prevent voluntary movement in most patients. However, in some cases (e.g., pediatric patients), it may be necessary to immobilize the patient by means of sedation. Using as short a scan time as possible helps minimize artifacts when scanning regions prone to movement. Respiratory motion can be minimized if patients are able to hold their breath for the duration of the scan. The sensitivity of the image to motion artifacts depends on the orientation of the motion. Therefore, it is preferable if the start and end position of the tube is aligned with the primary direction of motion, e.g., vertically above or below a patient undergoing a chest scan. Specifying body scan mode, as opposed to head scan mode, may automatically incorporate some motion artifact reduction in the reconstruction.[15],[18],[19],[20],[21]

   Scanner-Related Artifacts Top

Ring artifacts

Visible as concentric rings [Figure 4] centered around the location of the axis of rotation that result from imperfections in scanner detection or poor calibration. They are most prominent when homogeneous media are imaged. Owing to the circular trajectory and the discrete sampling process, these inconsistencies appear as rings in the planes coplanar with the movement plane of the source (axial planes in CBCT).[2],[10],[11],[12],[20],[22]
Figure 4: Ring artifact caused by calibration error

Click here to view

Avoidance and software corrections

The presence of circular artifacts in an image is an indication that the detector gain needs recalibration or may need repair services. Selecting the correct scan field of view may reduce the artifact [6] by using calibration data that fit more closely to the patient anatomy. All modern scanners use solid-state detectors, but their potential for ring artifacts is reduced by software that characterizes and corrects detector variations.[15]

   Image Noise Top

Noise is defined as an unwanted, randomly and/or non-randomly distributed disturbance of a signal that tends to obscure the signal's information content from the observer. Noise affects images produced by cone-beam CT units by reducing low contrast resolution [Figure 5],[23] making it difficult to differentiate low-density tissues, thereby reducing the ability to segment effectively. Some authors also include "detector blurring" in terms of noise. CBCT machines for dose reduction reasons are operated at milliamperes that are approximately one order of magnitude below those of medical CT machines. Thus, the signal-to-noise ratio is much lower than in CT. In other words, a high noise level is to be expected in CBCT images. Noise represents itself in inconsistent attenuation (gray) values in the projection images, i.e., large standard deviations in areas where a constant attenuation should be present. When back projecting these incorrect values, the computed attenuation coefficient "m" in the volume will also be erroneous. This is because of the use of an area detector, non-linear attenuation is recorded and contributes to image degradation or noise. The scatter-to-primary ratios are about 0.01 for single-ray CT and 0.05–0.15 for fan-beam and spiral CT, and may be as large as 0.4–2.0 in CBCT.[2],[10],[11],[12]
Figure 5: Noise

Click here to view


Scatter, on the other hand, is caused by those photons that are diffracted from their original path after interaction with matter. This additional share of scattered X-rays results in increased measured intensities, since the scattered intensities simply add to the primary intensity (I0). It is easy to see that back projection of overestimated intensities yields overestimated intensities in every voxel along the path; this corresponds to an underestimation of absorption. This effect has long been known for classical CT. The reconstructed error is dependent on the object and is proportional to the amount of scatter present. The larger the detector, the higher the probability that scattered photons incite it. Thus, the image degrading effect of scattered radiation will affect CBCT machines more than classical highly collimated fan-beam CTs. Scatter causes streak artifacts [24] in the reconstruction that are very similar to those caused by beam hardening. Scatter is well known to further reduce soft-tissue contrast and it will also affect the density values of all other tissues.[2],[10],[11],[12],[15],[25]

Extinction artifacts

These are often termed "missing value artifacts." If the object under study contains highly absorbing material, e.g., prosthetic gold restorations, then the signal IP recorded in the detector pixels behind that material may be close to zero or actually zero. A typical gold crown may be estimated to have at least 2–3 mm of thickness (when considering that the X-rays have to pass through both sides of it). This results in absorption of the mean energy of 90–97%. Clearly, two gold restorations or even one with thicker walls will result in zero incident intensity on the detector. Consequently, no absorption can be computed and severe artifacts are induced as these zero entries are back projected into the volume.[2],[10],[11],[12] Postprocessing image filters can help to correct raw data in areas of low photon count, identify portions of the raw projection data where there is a disproportionate loss in X-ray signal, and apply a local 3D filter with smoothing effect to reduce image noise and streak artifacts.[13],[15]

Exponential edge gradient effect

This effect appears at sharp edges with high contrast to neighboring structures. It is caused by averaging the measured intensity over a finite beam width (and finite focal spot width), while the mathematics used for the reconstruction assumes zero width. The width is determined by the focal spot and detector pixel size in combination with the imaging geometry of the machine. The exponential edge gradient effect (EEGE) error induced in the projection values has been proven to be negative always, i.e. it will always reduce the computed density value. The EEGE is known to cause streaks tangent to long straight edges in the projection direction. As sharp edges metallic FPD with of high contrast may commonly occur in the oral cavity, e.g., at metallic crown borders.[2],[10],[11],[12],[15]

Aliasing artifacts

Aliasing in CBCT lies in the divergence of the cone beam. In each projection, the voxels close to the source will be traversed by more recorded "rays" [Figure 6] than those close to the detector. This causes aliasing which represents itself as line patterns (moire patterns), commonly diverging toward the periphery of the reconstructed volume. Aliasing may also be introduced by a crude interpolation between the back projection "lines" and the voxel they traverse. Ideally, the exact volume a voxel shares with the "line-fragment" crossing through it should be used to compute the intensity of the voxel. Owing to computational limitations, however, often only crude but fast approximations (i.e., the length of the fragment) enter the computation. This causes aliasing artifacts which can be avoided by a better interpolation scheme that is more closely conforming with the actual physical measurement conditions.[2],[10],[11],[12]
Figure 6: Aliasing pattern artifact

Click here to view

Stair step artifacts

Stair step artifacts appear around the edges of structures in multiplanar and 3D reformatted images when wide collimations and non-overlapping reconstruction intervals are used. They are less severe with helical scanning, which permits reconstruction of overlapping sections without the extra dose to the patient that would occur if overlapping axial scans were obtained. Stair step artifacts are virtually eliminated in multiplanar and 3D reformatted images from thin section data obtained with today's multisection scanners.[1],[15]

Zebra artifacts

Faint stripes may be apparent in multiplanar and 3D reformatted

images from helical data because the helical interpolation process gives rise to a degree of noise inhomogeneity along the z axis. This "zebra" effect becomes more pronounced away from the axis of rotation because the noise inhomogeneity is worse off axis.[15]


Artifacts are common in today's CBCT. Since artifacts may interfere with the diagnostic process performed on CBCT data sets, every user should be aware of their presence. Consequently, more modern approaches attempt to avoid reconstruction errors by supplementing either missing information or incorrect information in the projection images. But all these require massive computational power, which has so far prevented them from being used in commercial scanners in daily routine work. The ever-increasing computational speed, however, and particularly the advancement in graphics processing units, has already drastically reduced the computational time required. As this process will continue, it is very likely that enhanced reconstruction methods will be much more common in the near future. They will help to reduce various sorts of artifacts.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

   References Top

Hunter AK, McDavid WD. Characterization and correction of cupping effect artefacts in cone beam CT. Dentomaxillofac Radiol 2012;41:217-23.  Back to cited text no. 1
Schulze R, Heil U, Groβ D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al. Artefacts in CBCT: A review. Dentomaxillofac Radiol 2011;40:265-73.  Back to cited text no. 2
Esmaeili F, Johari M, Haddadi P, Vatankhah M. Beam hardening artifacts: Comparison between two cone beam computed tomography scanners. J Dent Res Dent Clin Dent Prospects 2012;6:49-53.  Back to cited text no. 3
Ibraheem I. Reduction of artifacts in dental cone beam CT images to improve the three dimensional image reconstruction. J Biomed Sci Eng 2012;5:409-15.  Back to cited text no. 4
Esmaeili F, Johari M, Haddadi P. Beam hardening artifacts by dental implants: Comparison of cone-beam and 64-slice computed tomography scanners. Dent Res J (Isfahan) 2013;10:376-81.  Back to cited text no. 5
Nabha W, Hong YM, Cho JH, Hwang HS. Assessment of metal artifacts in three-dimensional dental surface models derived by cone-beam computed tomography. Korean J Orthod 2014;44:229-35.  Back to cited text no. 6
Jin SO, Kim JG, Lee SY, Kwon OK. Bone-induced streak artifact suppression in sparse-view CT image reconstruction. Biomed Eng Online 2012;11:44.  Back to cited text no. 7
Parirokh M, Ardjomand K, Manochehrifar H. Artifacts in cone-beam computed tomography of a post and core restoration: A case report. Iran Endod J 2012;7:98-101.  Back to cited text no. 8
Bechara B, McMahan CA, Geha H, Noujeim M. Evaluation of a cone beam CT artefact reduction algorithm. Dentomaxillofac Radiol 2012;41:422-8.  Back to cited text no. 9
Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dent Clin North Am 2008;52:707-30, v.  Back to cited text no. 10
Bhoosreddy AR, Sakhavalkar UP. Image deteriorating factors in cone beam computed tomography, their classification, measure to reduce them: A pictorial essay. J Indian Acad Oral Med Radiol 2014;26:293-7.  Back to cited text no. 11
  Medknow Journal  
Makins RS. Artifacts interfering with interpretation of cone beam computed tomography images. Dent Clin North Am 2014;58:485-95.  Back to cited text no. 12
Kataoka ML, Hochman MG, Rodriguez EK, Lin PJ, Kubo S, Raptopolous VD. A review of factors that affect artifact from metallic hardware on multi-row detector computed tomography. Curr Probl Diagn Radiol 2010;39:125-36.  Back to cited text no. 13
Leng S, Zambelli J, Tolakanahalli R, Nett B, Munro P, Star-Lack J, et al. Streaking artifacts reduction in four-dimensional cone-beam computed tomography. Med Phys 2008;35:4649-59.  Back to cited text no. 14
Barrett JF, Keat N. Artifacts in CT: Recognition and avoidance. Radiographics 2004;24:1679-91.  Back to cited text no. 15
Zheng D, Ford JC, Lu J, Lazos D, Hugo GD, Pokhrel D, et al. Bow-tie wobble artifact: Effect of source assembly motion on cone-beam CT. Med Phys 2011;38:2508-14.  Back to cited text no. 16
Spin-Neto R, Mudrak J, Matzen LH, Christensen J, Gotfredsen E, Wenzel A. Cone beam CT image artefacts related to head motion simulated by a robot skull: Visual characteristics and impact on image quality. Dentomaxillofac Radiol 2013;42:32310645.  Back to cited text no. 17
Zhang Q, Hu CY, Liu F, Goodman K, Rosenzweig EK, Mageras GS. Correction of motion artefacts in cone-beam CT using a patient-specific respiratory motion model. Med Phys 2010;37:2901-9.  Back to cited text no. 18
Sureshbabu W, Mawlawi O. PET/CT imaging artifact. J Nucl Med Technol 2005;33:156-61; quiz 163-4.  Back to cited text no. 19
LU W, Parikh PJ, Hubenschmidt JP, Politte DG, Whiting BR, Bradley JD, et al. Reduction of motion blurring artifacts using respiratory gated CT in sinogram space: A quantitative evaluation. Med Phys 2005;32:3295-304.  Back to cited text no. 20
Marchant TE, Price GJ, Matuszewski BJ, Moore CJ. Reduction of motion artefacts in on-board cone beam CT by warping of projection images. Br J Radiol 2011;84:251-64.  Back to cited text no. 21
Kincade K. Cone-beam CT artifacts: What's a dentist to do? Dentomaxillofac Radiol 2011;40:265-73.  Back to cited text no. 22
Bechara B, McMahan CA, Moore WS, Noujeim M, Geha H, Teixeira FB. Contrast-to-noise ratio difference in small field of view cone beam computed tomography machines. J Oral Sci 2012;54:227-32.  Back to cited text no. 23
Miracle AC, Mukherji SK. Conebeam CT of the head and neck, Part 1: Physical principles. AJNR Am J Neuroradiol 2009;30:1088-95.  Back to cited text no. 24
Zhao W, Zhu J, Wang L. Fast scatter artifacts correction for Cone-Beam CT without system modification and repeat scan. Med Phys 2015.  Back to cited text no. 25


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]

This article has been cited by
1 First clinical experience with a novel, mobile cone-beam CT system for treatment quality assurance in brachytherapy
Andre Karius, Vratislav Strnad, Michael Lotter, Stephan Kreppner, Christoph Bert
Strahlentherapie und Onkologie. 2022;
[Pubmed] | [DOI]
2 Temperature-Induced Ductile–Brittle Transition in Porous Carbonates and Change in Compaction Band Growth Revealed by 4-D X-Ray Tomography
Xiao Chen, Klaus Regenauer-Lieb, Hamid Roshan
Rock Mechanics and Rock Engineering. 2022;
[Pubmed] | [DOI]
3 Utilization of tin filters for streak artifact reduction in cone-beam computed tomography
Minsoo Chun, Jin Hwa Choi, Ohyun Kwon, Hyeongmin Jin, Sung Young Lee, Chang Heon Choi, Jung-in Kim, Jong Min Park
Journal of the Korean Physical Society. 2022;
[Pubmed] | [DOI]
4 The butterfly effect: improving brain cone-beam CT image artifacts for stroke assessment using a novel dual-axis trajectory
Nicole Mariantonia Cancelliere, Eric Hummel, Fred van Nijnatten, Peter van de Haar, Paul Withagen, Marijke van Vlimmeren, Bertan Hallacoglu, Ronit Agid, Patrick Nicholson, Vitor Mendes Pereira
Journal of NeuroInterventional Surgery. 2022; : neurintsur
[Pubmed] | [DOI]
5 Artifacts — A hitch in CBCT
Renita Castelino, Praveenkumar Ramdurg
IP International Journal of Maxillofacial Imaging. 2022; 7(4): 169
[Pubmed] | [DOI]
6 The Impact of Cone-Beam Computed Tomography Exposure Parameters on Peri-Implant Artifacts: A Literature Review
Pawel Sawicki, Pawel J Zawadzki, Piotr Regulski
Cureus. 2022;
[Pubmed] | [DOI]
7 Cone-beam computed tomography artifacts in the presence of dental implants and associated factors: an integrative review
Bianca Rodrigues Terrabuio,Caroline Gomes Carvalho,Mariela Peralta-Mamani,Paulo Sérgio da Silva Santos,Izabel Regina Fischer Rubira-Bullen,Cássia Maria Fischer Rubira
Imaging Science in Dentistry. 2021; 51(2): 93
[Pubmed] | [DOI]
8 Metal artifact reduction using common dental materials
Nicole V Hinchy,Nina K Anderson,Mina Mahdian
Dentomaxillofacial Radiology. 2021; : 20210302
[Pubmed] | [DOI]
9 Impact of Patient Alignment on Image Quality in C-Arm Computed Tomography – Evaluation Using an ACR Phantom
Babak Alikhani,Julius Renne,Sabine Maschke,Jan B. Hinrichs,Frank K. Wacker,Thomas Werncke
RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 2021; 193(04): 417
[Pubmed] | [DOI]
10 Artefact of fixed orthodontic auxiliary appliance in craniofacial CT image
Mahmud Mohammed,Norma Ab. Rahman,Ahmad Hadif Zaidin Samsudin
Orthodontic Waves. 2021; 80(1): 41
[Pubmed] | [DOI]
11 Use of cone-beam computed tomography for advanced imaging of the equine patient
Holly L. Stewart,Jeffery H. Siewerdsen,Brad B. Nelson,Christopher E. Kawcak
Equine Veterinary Journal. 2021;
[Pubmed] | [DOI]
12 Cone-Beam Computed Tomography in Endodontics—State of the Art
Jardel Francisco Mazzi-Chaves,Rafael Verardino Camargo,Aline Ferreira Borges,Ricardo Gariba Silva,Ruben Pauwels,Yara Teresinha Corrêa Silva-Sousa,Manoel Damião Sousa-Neto
Current Oral Health Reports. 2021; 8(2): 9
[Pubmed] | [DOI]
13 Artefacts at different distances from titanium and zirconia implants in cone-beam computed tomography: effect of tube current and metal artefact reduction
Arthur Xavier Maseti Mancini,Matheus Urias Cruz Santos,Hugo Gaêta-Araujo,Camila Tirapelli,Ruben Pauwels,Christiano Oliveira-Santos
Clinical Oral Investigations. 2021;
[Pubmed] | [DOI]
14 Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer
Rodrigo Delgadillo,Benjamin O. Spieler,John C. Ford,Deukwoo Kwon,Fei Yang,Matthew Studenski,Kyle R. Padgett,Matthew C. Abramowitz,Alan Dal Pra,Radka Stoyanova,Alan Pollack,Nesrin Dogan
Medical Physics. 2021; 48(5): 2386
[Pubmed] | [DOI]
15 Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients
Carmen Seller Oria,Adrian Thummerer,Jeffrey Free,Johannes A. Langendijk,Stefan Both,Antje C. Knopf,Arturs Meijers
Medical Physics. 2021;
[Pubmed] | [DOI]
16 Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach
Rohil Malpani, Christopher W. Petty, Junlin Yang, Neha Bhatt, Tal Zeevi, Vijay Chockalingam, Rajiv Raju, Alexandra Petukhova-Greenstein, Jessica Gois Santana, Todd R. Schlachter, David C. Madoff, Julius Chapiro, James Duncan, MingDe Lin
Journal of Vascular and Interventional Radiology. 2021;
[Pubmed] | [DOI]
17 Intraoperative cone beam computed tomography to improve outcomes after infra-renal endovascular aortic repair
Erol Lerisson, Benjamin O. Patterson, Adrien Hertault, Cedric Klein, François Pontana, Ibrahim Sediri, Stephan Haulon, Jonathan Sobocinski
Journal of Vascular Surgery. 2021;
[Pubmed] | [DOI]
18 Effect of artifact area on cone beam computed tomography scans when integrated with intraoral scans
Yan Biao,Dong-Wook Kim,Hyeon-Shik Hwang,Kyung-Min Lee
Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology. 2021; 131(4): 468
[Pubmed] | [DOI]
19 Surgical accuracy in 3D planned bimaxillary osteotomies: intraoral scans and plaster casts as digital dentition models
D.-M. Beek, F. Baan, J. Liebregts, S. Bergé, T. Maal, T. Xi
International Journal of Oral and Maxillofacial Surgery. 2021;
[Pubmed] | [DOI]
20 Magnetic resonance imaging versus cone beam computed tomography in diagnosis of periapical pathosis – A systematic review
N. Kiran Kumar,Seema Merwade,Pavithra Prabakaran,C.H. Laxmipriya,B.S. Annapoorna,C.N. Guruprasad
The Saudi Dental Journal. 2021;
[Pubmed] | [DOI]
21 Cone-beam Computed Tomographic–based Assessment of Filled C-shaped Canals: Artifact Expression of Cone-beam Computed Tomography as Opposed to Micro–computed Tomography and Nano–computed Tomography
Jardel Francisco Mazzi-Chaves,Karla de Faria Vasconcelos,Ruben Pauwels,Reinhilde Jacobs,Manoel Damião Sousa-Neto
Journal of Endodontics. 2020; 46(11): 1702
[Pubmed] | [DOI]
22 The role of radiomics in prostate cancer radiotherapy
Rodrigo Delgadillo,John C. Ford,Matthew C. Abramowitz,Alan Dal Pra,Alan Pollack,Radka Stoyanova
Strahlentherapie und Onkologie. 2020; 196(10): 900
[Pubmed] | [DOI]
23 Metal artifacts reduction in computed tomography: A phantom study to compare the effectiveness of metal artifact reduction algorithm, model-based iterative reconstruction, and virtual monochromatic imaging
Takuya Ishikawa,Shigeru Suzuki,Shingo Harashima,Rika Fukui,Masafumi Kaiume,Yoshiaki Katada
Medicine. 2020; 99(50): e23692
[Pubmed] | [DOI]
24 Cone-beam computed tomography for trauma
Saurabh Gupta,James R. Martinson,Daniel Ricaurte,Thomas M. Scalea,Jonathan J. Morrison
Journal of Trauma and Acute Care Surgery. 2020; 89(3): e34
[Pubmed] | [DOI]
25 Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy
Adrian Thummerer,Paolo Zaffino,Arturs Meijers,Gabriel Guterres Marmitt,Joao Seco,Roel J H M Steenbakkers,Johannes A Langendijk,Stefan Both,Maria F Spadea,Antje C Knopf
Physics in Medicine & Biology. 2020; 65(9): 095002
[Pubmed] | [DOI]
26 Contributions to the study of common artifacts and errors in conventional and three-dimensional radio-imaging used in the evaluation of odontal, endodontic and periodontal pathology.
Diana-Florina Kulcsar,Oana Elena Stoica,Monica Dana Monea,Alexandra Mihaela Stoica
Acta Stomatologica Marisiensis Journal. 2020; 3(2): 9
[Pubmed] | [DOI]
27 Comparing the Diagnostic Accuracy of CBCT Grayscale Values with DXA Values for the Detection of Osteoporosis
Mohammed G. Sghaireen,Kiran Kumar Ganji,Mohammad Khursheed Alam,Kumar Chandan Srivastava,Deepti Shrivastava,Saifulizan Ab Rahman,Santosh R. Patil,Selham Al Habib
Applied Sciences. 2020; 10(13): 4584
[Pubmed] | [DOI]
28 Impacts of Thresholds of Gray Value for Cone-Beam Computed Tomography 3D Reconstruction on the Accuracy of Image Matching with Optical Scan
Se-Won Park,Ra Gyoung Yoon,Hyunwoo Lee,Heon-Jin Lee,Yong-Do Choi,Du-Hyeong Lee
International Journal of Environmental Research and Public Health. 2020; 17(17): 6375
[Pubmed] | [DOI]
29 The efficacy of metal artifact reduction (MAR) algorithm in cone-beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants
Mahnaz Sheikhi,Parichehr Behfarnia,Mahdis Mostajabi,Naeimeh Nasri
Journal of Periodontology. 2019;
[Pubmed] | [DOI]
30 Accuracy of in vitro mandibular volumetric measurements from CBCT of different voxel sizes with different segmentation threshold settings
Ting Dong,Lunguo Xia,Chenglin Cai,Lingjun Yuan,Niansong Ye,Bing Fang
BMC Oral Health. 2019; 19(1)
[Pubmed] | [DOI]
31 Re-exposure in cone beam CT of the dentomaxillofacial region: a retrospective study
Yasamin Habibi,Edriss Habibi,Bilal Al-Nawas
Dentomaxillofacial Radiology. 2019; : 20180184
[Pubmed] | [DOI]
32 Application of iterative reconstruction algorithms to mitigate CT-artefacts when measuring fiber reinforced polymer materials
Arnold Wilbers,Ander Biguri,Jennifer Schillings,Joachim Loos
Polymer. 2019; 177: 120
[Pubmed] | [DOI]
33 Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images
Yi Fan,Richard Beare,Harold Matthews,Paul Schneider,Nicky Kilpatrick,John Clement,Peter Claes,Anthony Penington,Christopher Adamson
Dentomaxillofacial Radiology. 2018; : 20180261
[Pubmed] | [DOI]
34 Metallic materials in the exomass impair cone beam CT voxel values
Amanda P Candemil,Benjamin Salmon,Deborah Q Freitas,Glaucia MB Ambrosano,Francisco Haiter-Neto,Matheus L Oliveira
Dentomaxillofacial Radiology. 2018; : 20180011
[Pubmed] | [DOI]
35 Surface radiation dose comparison of a dedicated extremity cone beam computed tomography (CBCT) device and a multidetector computed tomography (MDCT) machine in pediatric ankle and wrist phantoms
Sebastian Tschauner,Robert Marterer,Eszter Nagy,Georg Apfaltrer,Michael Riccabona,Georg Singer,Georg Stücklschweiger,Helmuth Guss,Erich Sorantin,Hajo Zeeb
PLOS ONE. 2017; 12(6): e0178747
[Pubmed] | [DOI]
36 The Use of Cone-Beam Computed Tomography in Management of Patients Requiring Dental Implants: An American Academy of Periodontology Best Evidence Review
Hector F. Rios,Wenche S. Borgnakke,Erika Benavides
Journal of Periodontology. 2017; 88(10): 946
[Pubmed] | [DOI]
37 The Effect of Implant-Induced Artifacts on Interpreting Adjacent Bone Structures on Cone-Beam Computed Tomography Scans
Rachel A. Sheridan,Yi-Chen Chiang,Ann M. Decker,Pimchanok Sutthiboonyapan,Hsun-Liang Chan,Hom-Lay Wang
Implant Dentistry. 2017; : 1
[Pubmed] | [DOI]
38 CT reconstruction and MRI fusion of 3D rotational angiography in the evaluation of pediatric cerebrovascular lesions
Prakash Muthusami,Nicholas Shkumat,Vanessa Rea,Albert H. Chiu,Manohar Shroff
Neuroradiology. 2017;
[Pubmed] | [DOI]
39 Dual-energy imaging method to improve the image quality and the accuracy of dose calculation for cone-beam computed tomography
Kuo Men,Jianrong Dai,Xinyuan Chen,Minghui Li,Ke Zhang,Peng Huang
Physica Medica. 2017; 36: 110
[Pubmed] | [DOI]
40 Quantitative assessment of image artifacts from root filling materials on CBCT scans made using several exposure parameters
Katharina Alves Rabelo,Yuri Wanderley Cavalcanti,Martina Gerlane de Oliveira Pinto,Saulo Leonardo Sousa Melo,Paulo Sérgio Flores Campos,Luciana Soares de Andrade Freitas Oliveira,Daniela Pita de Melo
Imaging Science in Dentistry. 2017; 47(3): 189
[Pubmed] | [DOI]
41 Comparative Evaluation of the Artefacts Index of Dental Materials on Two-Dimensional Cone-beam Computed Tomography
Fusong Yuan,Litong Chen,Xiaofei Wang,Yong Wang,Peijun Lyu,Yuchun Sun
Scientific Reports. 2016; 6: 26107
[Pubmed] | [DOI]


Print this article  Email this article
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Article in PDF (985 KB)
    Citation Manager
    Access Statistics
    Reader Comments
    Email Alert *
    Add to My List *
* Registration required (free)  

   X-Ray Beam Artifacts
    Patient-Related ...
    Scanner-Related ...
   Image Noise
    Article Figures

 Article Access Statistics
    PDF Downloaded694    
    Comments [Add]    
    Cited by others 41    

Recommend this journal