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.

scholarly journals Modified Depth-Map Inpainting Method Using the Neural Network

2019 ◽  
Vol 224 ◽  
pp. 04005
Author(s):  
Nikolay Gapon ◽  
Roman Sizyakin ◽  
Marina Zhdanova ◽  
Oksana Balabaeva ◽  
Yigang Cen
Keyword(s):  
Neural Network ◽  
Geometric Model ◽  
Similarity Criterion ◽  
Depth Map ◽  
Experimental Results ◽  
Stereo Pair ◽  
Image Structure ◽  
The Neural Network ◽  
Modern Methods ◽  
Trained Neural Network

This paper proposes a method for reconstructing a depth map obtained using a stereo pair image. The proposed approach is based on a geometric model for the synthesis of patches. The entire image is preliminarily divided into blocks of different size, where large blocks are used to restore homogeneous areas, and small blocks are used to restore details of the image structure. Lost pixels are recovered by copying the pixel values from the source based on the similarity criterion. We used a trained neural network to select the “best like” patch. Experimental results show that the proposed method gives better results than other modern methods, both in subjective and objective measurements for reconstructing a depth map.

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 image reconstruction algorithm for CT of implanted metal orthopedic devices: a work in progress

2009 ◽  
Vol 38 (8) ◽  
pp. 797-802 ◽  
Author(s):  
Patrick T. Liu ◽  
William P. Pavlicek ◽  
Mary B. Peter ◽  
Mark J. Spangehl ◽  
Catherine C. Roberts ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Image Reconstruction Algorithm ◽  
Work In Progress

SU-DD-A3-04: Evaluation of Metal Artifact Reduction Using MVCT and Model Based Image Reconstruction

2010 ◽  
Vol 37 (6Part12) ◽  
pp. 3091-3091
Author(s):  
M Paudel ◽  
P Kirvan ◽  
BG Fallone ◽  
S Rathee
Keyword(s):  
Image Reconstruction ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Model Based
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