Name
Abstract | ||
|---|---|---|
Artefacts caused by the presence of metallic implants and prosthesis appear as dark and bright streaks in computed tomography (CT) images, that obscure the information about underlying anatomical structures. These phenomena can severely degrade the image quality and hinder the correct diagnostic interpretation. Although many techniques for the reduction of metal artefacts have been proposed in literature, their effectiveness is still limited. In this paper, an application of a convolutional neural networks (CNN) to the problem of metal artefact reduction (MAR) in the image domain is investigated. Experimental results show that image-domain CNN can substantially suppresses streaking artefacts in the reconstructed images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/MIPRO.2019.8756770 | 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) |
Keywords | Field | DocType |
computed tomography,metal artefacts,convolutional neural networks | Computer vision,Computer science,Convolutional neural network,Image quality,Computer network,Artificial intelligence,Computed tomography,Anatomical structures,Streaking | Conference |
ISBN | Citations | PageRank |
978-1-5386-9296-7 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amira Serifovic-Trbalic | 1 | 11 | 4.42 |
| Amira Serifovic-Trbalic | 2 | 11 | 4.42 |
| Damir Demirovic | 3 | 9 | 3.60 |
| Emir Skejic | 4 | 0 | 1.35 |
| D. Gleich | 5 | 0 | 0.34 |