CT colonography (CTC) uses orally administered fecal-tagging agents to indicate residual materials that could otherwise interfere with the interpretation of CTC images. To visualize the colon in virtual 3D endoluminal views, electronic cleansing (EC) can be used to subtract the fecal-tagged materials from the CTC images. However, conventional EC methods produce subtraction artifacts that distract readers and computer-aided detection systems. In this study, we used generative adversarial learning to transform fecal-tagged CTC input image volumes to corresponding virtually cleansed image volumes. To overcome the need for paired training samples, we used a cycle-consistent 3D-generative adversarial network (3D EC-cycleGAN) scheme that can be trained with unpaired samples. The associated generator and discriminator networks were implemented as 3D-convolutional networks, and the loss functions were adapted to the unique requirements of EC in CTC. To investigate the feasibility of the approach, the 3D EC-cycleGAN was trained and tested with CTC image volumes of an anthropomorphic phantom filled partially with fecal tagging to recreate the attenuation ranges observed in clinical CTC. Our preliminary results indicate that the proposed 3D EC-cycleGAN can potentially learn to perfor

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492Z (15 March 2019); doi: 10.1117/12.2512599

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)