Metal artifact reduction for practical dental computed tomography by improving interpolation‐based reconstruction with deep learning

2019 ◽  
Vol 46 (12) ◽  
Author(s):  
Kaichao Liang ◽  
Li Zhang ◽  
Hongkai Yang ◽  
Yirong Yang ◽  
Zhiqiang Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Dental Computed Tomography

Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography

2018 ◽  
Vol 45 (2) ◽  
pp. 714-724 ◽  
Author(s):  
Mohamed A. A. Hegazy ◽  
Mohamed Elsayed Eldib ◽  
Daniel Hernandez ◽  
Myung Hye Cho ◽  
Min Hyoung Cho ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dual Energy ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Dental Computed Tomography

A Normalized Metal Artifact Reduction Method Using an Artifact-Reduced Prior for Dental Computed Tomography

2019 ◽  
Vol 74 (3) ◽  
pp. 298-304
Author(s):  
Chulkyu Park ◽  
Dongyeon Lee ◽  
Younghwan Lim ◽  
Guna Kim ◽  
Kyuseok Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Reduction Method ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Dental Computed Tomography

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.

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