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Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-7750-1917
2018 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 5, no 2, article id 024006Article in journal (Refereed) Published
Abstract [en]

Percutaneous coronary intervention (PCI) uses x-ray images, which may give high radiation dose and high concentrations of contrast media, leading to the risk of radiation-induced injury and nephropathy. These drawbacks can be reduced by using lower doses of x-rays and contrast media, with the disadvantage of noisier PCI images with less contrast. Vessel-edge-preserving convolutional neural networks (CNN) were designed to denoise simulated low x-ray dose PCI images, created by adding artificial noise to high-dose images. Objective functions of the designed CNNs have been optimized to achieve an edge-preserving effect of vessel walls, and the results of the proposed objective functions were evaluated qualitatively and quantitatively. Finally, the proposed CNN-based method was compared with two state-of-the-art denoising methods: K-SVD and block-matching and 3D filtering. The results showed promising performance of the proposed CNN-based method for PCI image enhancement with interesting capabilities of CNNs for real-time denoising and contrast enhancement tasks.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 5, no 2, article id 024006
National Category
Computer Vision and Robotics (Autonomous Systems) Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:kth:diva-232924DOI: 10.1117/1.JMI.5.2.024006ISI: 000439291500031PubMedID: 29963578Scopus ID: 2-s2.0-85049396885OAI: oai:DiVA.org:kth-232924DiVA, id: diva2:1237279
Note

QC 20180808

Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-11-23Bibliographically approved

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Smedby, Örjan

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