Normalized Metal Artifact Reduction in Head and Neck Computed Tomography

2012 ◽  
Vol 47 (7) ◽  
pp. 415-421 ◽  
Author(s):  
Michael M. Lell ◽  
Esther Meyer ◽  
Michael A. Kuefner ◽  
Matthias S. May ◽  
Rainer Raupach ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Head And Neck ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

scholarly journals Detectability of Head and Neck Cancer via New Computed Tomography Reconstruction Tools including Iterative Reconstruction and Metal Artifact Reduction

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2154
Author(s):  
Daniel Troeltzsch ◽  
Seyd Shnayien ◽  
Max Heiland ◽  
Kilian Kreutzer ◽  
Jan-Dirk Raguse ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Iterative Reconstruction ◽  
State Of The Art ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Recurrent Head

State-of-the-art technology in Computed Tomography (CT) includes iterative reconstruction algorithms (IR) and metal artefact reduction (MAR) techniques. The objective of the study is to show the benefits of this technology for the detection of primary and recurrent head and neck cancer. A total of 131 patients underwent contrast-enhanced CT for diagnosis of primary and recurrent Head and Neck cancer; 110 patients were included. All scans were reconstructed using iterative reconstruction, and metal artifact reduction was applied when indicated. Tumor detectability was evaluated dichotomously. Histopathological findings were used as a standard of reference. Data were analyzed retrospectively, statistics was performed through diagnostic test characteristics. State-of-the-art Head and Neck CT showed a sensitivity of 0.83 (95% CI; 0.61–0.95) with 0.93 specificity (95% CI; 0.84–0.98) for primary tumor detection. Recurrent tumors were identified with a 0.94 sensitivity (95% CI; 0.71–0.99) and 0.93 specificity (95% CI; 0.84–0.98) in this study. Conclusion: State-of-the-art reconstruction tools improve the diagnostic quality of Head and Neck CT, especially for recurrent tumor detection, compared with data published for standard CT. IR and MAR are easily implemented in routine clinical settings and improve image evaluation by reducing artifacts and image noise while lowering radiation exposure.

scholarly journals Metal artifact reduction in computed tomography images based on developed generative adversarial neural network

2021 ◽  
Vol 24 ◽  
pp. 100573
Author(s):  
Goli Khaleghi ◽  
Mohammad Hosntalab ◽  
Mahdi Sadeghi ◽  
Reza Reiazi ◽  
Seied Rabi Mahdavi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

Metal Artifact Reduction on Chest Computed Tomography Examinations

2017 ◽  
Vol 41 (3) ◽  
pp. 446-454 ◽  
Author(s):  
Julien Pagniez ◽  
Louise Legrand ◽  
Suonita Khung ◽  
Jean-Baptiste Faivre ◽  
Alain Duhamel ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Chest Computed Tomography

Metal Artifact Reduction With Tin Prefiltration in Computed Tomography

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Carsten Hackenbroch ◽  
Simone Schüle ◽  
Daniel Halt ◽  
Laura Zengerle ◽  
Meinrad Beer
Keyword(s):  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

A fully 3D approach for metal artifact reduction in computed tomography

2012 ◽  
Vol 39 (11) ◽  
pp. 7042-7054 ◽  
Author(s):  
Bärbel Kratz ◽  
Imke Weyers ◽  
Thorsten M. Buzug
Keyword(s):  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

scholarly journals Effect of metal artifact reduction software on image quality of C-arm cone-beam computed tomography during intracranial aneurysm treatment

2018 ◽  
Vol 24 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Yukiko Enomoto ◽  
Keita Yamauchi ◽  
Takahiko Asano ◽  
Katharina Otani ◽  
Toru Iwama
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Intracranial Aneurysms ◽  
Clinical Findings ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Cbct Image

Background and purpose C-arm cone-beam computed tomography (CBCT) has the drawback that image quality is degraded by artifacts caused by implanted metal objects. We evaluated whether metal artifact reduction (MAR) prototype software can improve the subjective image quality of CBCT images of patients with intracranial aneurysms treated with coils or clips. Materials and methods Forty-four patients with intracranial aneurysms implanted with coils (40 patients) or clips (four patients) underwent one CBCT scan from which uncorrected and MAR-corrected CBCT image datasets were reconstructed. Three blinded readers evaluated the image quality of the image sets using a four-point scale (1: Excellent, 2: Good, 3: Poor, 4: Bad). The median scores of the three readers of uncorrected and MAR-corrected images were compared with the paired Wilcoxon signed-rank and inter-reader agreement of change scores was assessed by weighted kappa statistics. The readers also recorded new clinical findings, such as intracranial hemorrhage, air, or surrounding anatomical structures on MAR-corrected images. Results The image quality of MAR-corrected CBCT images was significantly improved compared with the uncorrected CBCT image ( p < 0.001). Additional clinical findings were seen on CBCT images of 70.4% of patients after MAR correction. Conclusion MAR software improved image quality of CBCT images degraded by metal artifacts.

Customized Total Variation Algorithm for Metal Artifact Reduction in Computed Tomography

2021 ◽  
Author(s):  
Ziheng Deng ◽  
Yufu Zhou ◽  
Weikang Zhang ◽  
Zefan Lin ◽  
Jun Zhao
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction
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