Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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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))
  • Received : 2019.02.08
  • Accepted : 2019.03.26
  • Published : 2019.03.29
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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

CPTSCQ_2019_v24n3_41_f0001.png 이미지

Fig. 1. Research work flow of contrast enhanced CT image generation.

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Fig. 2. CT and contrast enhanced CT image information.

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Fig. 3. Scheme of image generative deep learning network with generator and discriminator

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Fig. 4. Represent of CT and contrast enhanced CT image

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Fig. 5. Generation of contrast enhanced CT results. (A) CT image (input data), (B) Contrast enhanced CT image (target data), (C) generated CT image without histogram equalization, (D) generated result image with histogram equalization.

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Fig. 6. Paired sample t-test of synthesized contrast enhanced CT image with and without histogram equalization.

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Fig. 7. Lymph node short and long axis ratio (S/L) of CT, contrast enhanced CT, and generated contrast enhancedCT image.

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