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Abstract
Objective.To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector.Approach.Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network.Main results.Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs. Quantitative results and clinical evaluation from radiologists also show that our method can provide accurate translation results.Significance.Quantitative evaluation of CT images from multi-site or longitudinal studies has been a difficult problem due to the image variation depending on CT scan parameters and manufacturers. The proposed method can be utilized to address this for the quantitative analysis of multi-domain CT images.

Affiliations

Hyunjong Kim 1, Gyutaek Oh 2, Joon Beom Seo 3, Hye Jeon Hwang 3, Sang Min Lee 3, Jihye Yun 4, Jong Chul Ye 5
1Robotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
2Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
3Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Republic of Korea.
4Convergence Medicine, Biomedical Engineering Research Center University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Republic of Korea.
5Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.