본문 바로가기
  • Home

Generation of contrast enhanced computed tomography image using deep learning network

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(3), pp.41-47
  • DOI : 10.9708/jksci.2019.24.03.041
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 8, 2019
  • Accepted : March 26, 2019
  • Published : March 29, 2019

SangKeun Woo 1

1한국원자력의학원

Accredited

ABSTRACT

In this paper, we propose a application of conditional generative adversarial network (cGAN) for generation of contrast enhanced computed tomography (CT) image. Two types of CT data which were the enhanced and non-enhanced were used and applied by the histogram equalization for adjusting image intensities. In order to validate the generation of contrast enhanced CT data, the structural similarity index measurement (SSIM) was performed. Prepared generated contrast CT data were analyzed the statistical analysis using paired sample t-test. In order to apply the optimized algorithm for the lymph node cancer, they were calculated by short to long axis ratio (S/L) method. In the case of the model trained with CT data and their histogram equalized SSIM were 0.905±0.048 and 0.908±0.047. The tumor S/L of generated contrast enhanced CT data were validated similar to the ground truth when they were compared to scanned contrast enhanced CT data. It is expected that advantages of Generated contrast enhanced CT data based on deep learning are a cost-effective and less radiation exposure as well as further anatomical information with non-enhanced CT data.

Citation status

* References for papers published after 2022 are currently being built.