scholarly journals The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review

2021 ◽  
Author(s):  
Sathyathas Puvanasunthararajah ◽  
Davide Fontanarosa ◽  
Marie‐Luise Wille ◽  
Saskia M. Camps
Keyword(s):  
Computed Tomography ◽  
Literature Review ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Scans ◽  
Reduction Methods

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

Comprehensive Survey on Metal Artifact Reduction Methods in Computed Tomography Images

2017 ◽  
pp. 1281-1302 ◽  
Author(s):  
Shrinivas D. Desai ◽  
Linganagouda Kulkarni
Keyword(s):  
Medical Imaging ◽  
Ct Images ◽  
Future Research ◽  
Reference Source ◽  
Projection Data ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reduction Methods ◽  
Comprehensive Survey

Over the past few years, medical imaging technology has significantly advanced. Today, medical imaging modalities have been designed with state-of-the-art technology to provide much better in-depth resolution, reduced artifacts, and improved contrast –to – noise ratio. However in many practical situations complete projection data is not acquired leading to incomplete data problem. When the data is incomplete, tomograms may blur, resolution degrades, noise increases and forms artifacts which is the most important factor in degrading the tomography image quality and eventually hinders diagnostic accuracy. Efficient strategies to address this problem and to improve the diagnostic acceptability of CT images are thus invaluable. This review work, presents comprehensive survey of techniques for minimization of streaking artifact due to metallic implant in CT images. Problematic issues and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in metal artifact reduction methods.

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