Publication detail

Deep learning based prediction of virtual non contrast CT images

JAKUBÍČEK, R. CHMELÍK, J. VIČAR, T. OUŘEDNÍČEK, P. JAN, J.

Original Title

Deep learning based prediction of virtual non contrast CT images

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Spectral CT; dual-energy; virtual non-contrast image; CNN; GAN

Authors

JAKUBÍČEK, R.; CHMELÍK, J.; VIČAR, T.; OUŘEDNÍČEK, P.; JAN, J.

Released

19. 2. 2021

Publisher

Assiciation for Computing Machinery

Location

New York, NY, USA

ISBN

978-1-4503-8983-9

Book

International Conference Proceeding Series (ICPS) - ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering

ISBN

2168-4081

Periodical

ACM Proceedings

State

United States of America

Pages from

72

Pages to

76

Pages count

5

URL

https://dl.acm.org/doi/10.1145/3459104.3460237

BibTex

@inproceedings{BUT160561,
  author="Roman {Jakubíček} and Jiří {Chmelík} and Tomáš {Vičar} and Petr {Ouředníček} and Jiří {Jan}",
  title="Deep learning based prediction of virtual non contrast CT images",
  booktitle="International Conference Proceeding Series (ICPS) - ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering",
  year="2021",
  journal="ACM Proceedings",
  pages="72--76",
  publisher="Assiciation for Computing Machinery",
  address="New York, NY, USA",
  doi="10.1145/3459104.3460237",
  isbn="978-1-4503-8983-9",
  issn="2168-4081",
  url="https://dl.acm.org/doi/10.1145/3459104.3460237"
}