scholarly journals Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear

2018 ◽  
pp. 3-11 ◽  
Author(s):  
Jianing Wang ◽  
Yiyuan Zhao ◽  
Jack H. Noble ◽  
Benoit M. Dawant
Keyword(s):  
Ct Images ◽  
Generative Adversarial Networks ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Adversarial Networks

scholarly journals Inner-ear augmented metal artifact reduction with simulation-based 3D generative adversarial networks

2021 ◽  
Vol 93 ◽  
pp. 101990
Author(s):  
Zihao Wang ◽  
Clair Vandersteen ◽  
Thomas Demarcy ◽  
Dan Gnansia ◽  
Charles Raffaelli ◽  
...  
Keyword(s):  
Inner Ear ◽  
Generative Adversarial Networks ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Adversarial Networks ◽  
Simulation Based

Segmentation-aided adaptive filtering for metal artifact reduction in radio-therapeutic CT images

2004 ◽  
Author(s):  
Celine Saint Olive ◽  
Michael R. Kaus ◽  
Vladimir Pekar ◽  
Kai Eck ◽  
Lothar Spies
Keyword(s):  
Adaptive Filtering ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction

scholarly journals Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1629
Author(s):  
Tsutomu Gomi ◽  
Rina Sakai ◽  
Hidetake Hara ◽  
Yusuke Watanabe ◽  
Shinya Mizukami
Keyword(s):  
Radiation Dose ◽  
Dose Reduction ◽  
Gumbel Distribution ◽  
Radiation Dose Reduction ◽  
Generative Adversarial Networks ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Digital Tomosynthesis ◽  
The Novel ◽  
Adversarial Networks

In this study, a novel combination of hybrid generative adversarial networks (GANs) comprising cycle-consistent GAN, pix2pix, and (mask pyramid network) MPN (CGpM-metal artifact reduction [MAR]), was developed using projection data to reduce metal artifacts and the radiation dose during digital tomosynthesis. The CGpM-MAR algorithm was compared with the conventional filtered back projection (FBP) without MAR, FBP with MAR, and convolutional neural network MAR. The MAR rates were compared using the artifact index (AI) and Gumbel distribution of the largest variation analysis using a prosthesis phantom at various radiation doses. The novel CGpM-MAR yielded an adequately effective overall performance in terms of AI. The resulting images yielded good results independently of the type of metal used in the prosthesis phantom (p < 0.05) and good artifact removal at 55% radiation-dose reduction. Furthermore, the CGpM-MAR represented the minimum in the model with the largest variation at 55% radiation-dose reduction. Regarding the AI and Gumbel distribution analysis, the novel CGpM-MAR yielded superior MAR when compared with the conventional reconstruction algorithms with and without MAR at 55% radiation-dose reduction and presented features most similar to the reference FBP. CGpM-MAR presents a promising method for metal artifact and radiation-dose reduction in clinical practice.

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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