Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis

2020 ◽  
Author(s):  
Tsutomu Gomi ◽  
Hidetake Hara ◽  
Yusuke Watanabe ◽  
Shinya Mizukami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Digital Tomosynthesis ◽  
Metal Artifact Reduction

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

scholarly journals Metal artifact reduction on MRI images

2018 ◽  
Vol 226 ◽  
pp. 04048
Author(s):  
Nikolay V. Gapon ◽  
Evgenii A. Semenishchev ◽  
Oxana S. Balabaeva ◽  
Arina A. Skorikova ◽  
Olga A. Tokareva ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
State Of The Art ◽  
Geometric Model ◽  
Similarity Criterion ◽  
Experimental Results ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Trained Neural Network

This article examines the method of image reconstruction, which aims to restore the exposed areas on MRI images. The algorithm is based on a geometric model for patch synthesis. The lost pixels are recovered by copying pixel values from the source using a similarity criterion. We used a trained neural network to choose the “best similar” patch. Experimental results show that the proposed method outperforms widely used state-of-the-art methods.

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