[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9708/jksci.2019.24.03.041

Generation of contrast enhanced computed tomography image using deep learning network

Woo, Sang-Keun (Division of applied RI, Korea Institute of Radiological and Medical Sciences (KIRAMS), Radiological and Medico-Oncological Sciences, University of Science and Technology (UST))

Publication Information

Journal of the Korea Society of Computer and Information / v.24, no.3, 2019 , pp. 41-47 More about this Journal

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 $0.905{\pm}0.048$ and $0.908{\pm}0.047$ . 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.

Keywords

Contrast enhanced computed tomography; deep learning; generative adversarial network;

Citations & Related Records

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