Deep learning based prediction of virtual non contrast CT images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU134358" target="_blank" >RIV/00216305:26220/21:PU134358 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3459104.3460237" target="_blank" >https://dl.acm.org/doi/10.1145/3459104.3460237</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3459104.3460237" target="_blank" >10.1145/3459104.3460237</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning based prediction of virtual non contrast CT images
Original language description
In this paper, we present a method, based on deep learning, for prediction of non-contrast CT image from a single contrast image. For training of this image-to-image translation task, virtual contrast and virtual non-contrast (VNC) images were created from spectral CT data by Philips IntelliSpace Portal (ISP) software. Virtual version of conventional CT (cCT) images and VNC images allows to train paired supervised image-to-image translation models. Two different schemes were tested to train the Convolutional Neural Network (CNN) with U-Net architecture, using standard training with L1/L2 loss as well as training via conditional Generative Adversarial Network (cGAN) with an additional Wasserstein modification (WcGAN). Qualitatively, the proposed method achieves similar results to the original VNC images. However, quantitatively, the trained CNN provides a slightly smaller density reduction in some tissues. The advantage of this approach is that non-contrast image can be predicted from a single conventional CT image, without the need for pre- and post-contrast scan or without a spectral CT scan.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Conference Proceeding Series (ICPS) - ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering
ISBN
978-1-4503-8983-9
ISSN
2168-4081
e-ISSN
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Number of pages
5
Pages from-to
72-76
Publisher name
Assiciation for Computing Machinery
Place of publication
New York, NY, USA
Event location
Prague
Event date
Nov 22, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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