Neural Contrast Enhancement of CT Image
- Title
- Neural Contrast Enhancement of CT Image
- Authors
- 서민교
- Date Issued
- 2023
- Publisher
- 포항공과대학교
- Abstract
- Contrast Enhanced Computed Tomography (CECT) images obtained in this way
are more useful than Non-Enhanced Computed Tomography (NECT) images for med-
ical diagnosis, but not available for everyone due to side effects of the contrast ma-
terials. Motivated by this, we develop a neural network that takes NECT images and
generates their CECT counterparts. Learning such a network is extremely challenging
since NECT and CECT images for training are not aligned even at the same location
of the same patient due to movements of internal organs. We propose a two-stage
framework to address this issue. The first stage trains an auxiliary network that re-
moves the effect of contrast enhancement in CECT images to synthesize their NECT
counterparts well-aligned with them. In the second stage, the target model is trained to
predict the real CECT images given a synthetic NECT image as input. Experimental
results and analysis by physicians on abdomen CT images suggest that our method
outperforms existing models for neural image synthesis.
- URI
- http://postech.dcollection.net/common/orgView/200000692638
https://oasis.postech.ac.kr/handle/2014.oak/118446
- Article Type
- Thesis
- Files in This Item:
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