scholarly journals Streak Metal Artifact Reduction Technique in Cone Beam Computed Tomography Images after Endovascular Neurosurgery

2021 ◽  
Author(s):  
Takafumi OTSUKA ◽  
Masahiro NISHIHORI ◽  
Takashi IZUMI ◽  
Takeshi UEMURA ◽  
Takashi SAKAI ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Reduction Technique ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

The performance of metal artifact reduction algorithms in cone beam computed tomography images considering the effects of materials, metal positions, and fields of view

2019 ◽  
Vol 127 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Karla de Faria Vasconcelos ◽  
Marina Codari ◽  
Polyane Mazucatto Queiroz ◽  
Laura Ferreira Pinheiro Nicolielo ◽  
Deborah Queiroz Freitas ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

Metal artifact reduction in cone beam computed tomography using forward projected reconstruction information

2011 ◽  
Vol 21 (3) ◽  
pp. 174-182 ◽  
Author(s):  
Manuel Meilinger ◽  
Christian Schmidgunst ◽  
Oliver Schütz ◽  
Elmar W. Lang
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

scholarly journals Efficacy of metal artifact reduction algorithm of cone-beam computed tomography for detection of fenestration and dehiscence around dental implants (Preprint)

2020 ◽  
Author(s):  
Fatemeh Salemi ◽  
Mohamad Reza Jamalpour ◽  
Amir Eskandarloo ◽  
Leili Tapak ◽  
Narges Rahimi
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Roc Curve ◽  
Titanium Implants ◽  
Cone Beam ◽  
Kappa Statistics ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Sensitivity Specificity

UNSTRUCTURED This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence. Thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants using a round bur. The bone blocks were then mounted in a wax rim to simulate the mandible. CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm before and after creation of defects. Two experienced radiologists observed the images twice with a 2-week interval. Data were analyzed using SPSS software version 22.The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy of different imaging modalities were calculated and analyzed. According to the kappa statistics, the intra- and inter-observer agreements were higher for images without the MAR algorithm compared with those with the MAR algorithm. In both CBCT systems, use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for detection of fenestration and dehiscence. The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm. Although CBCT is suitable for detection of peri-implant defects, application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence.

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