Self-supervised generative adversarial network for electronic cleansing in dual-energy CT colonography

CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the complete region of colon without possibly obstructing residual materials inside the colon, an orally administered contrast agent is used to opacify the residual fecal materials on CT images followed by virtual cleansing of the opacified materials from the images. However, current EC methods can introduce large numbers of residual image artifacts that complicate the interpretation of the virtually cleansed CTC images. Such artifacts can be resolved by use of dual-energy CTC (DE-CTC) that provides more information about the observed materials than does conventional single-energy CTC (SE-CTC). We generalized a 3D generative adversarial network (3D-GAN) model into a self-supervised electronic cleansing (EC) scheme for dual-energy CT colonography (DE-CTC). The 3D-GAN is used to transform the acquired DE-CTC volumes into a representative cleansed CTC volume by use of an iterative self-supervised method that adapts the scheme to the unique conditions of each case. Our preliminary evaluation with an anthropomorphic phantom indicated that the use of the 3DGAN EC scheme with DE-CTC features and the self-supervised scheme generates EC images of higher quality than those obtained by use of SE-CTC or conventional training samples only.