CT colonography (CTC) uses abdominal CT scans to examine the colon for cancers and polyps. To visualize the completeregion of colon without possibly obstructing residual materials inside the colon, an orally administered contrast agent isused to opacify the residual fecal materials on CT images followed by virtual cleansing of the opacified materials from theimages. However, current EC methods can introduce large numbers of residual image artifacts that complicate theinterpretation 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). Wegeneralized a 3D generative adversarial network (3D-GAN) model into a self-supervised electronic cleansing (EC) schemefor dual-energy CT colonography (DE-CTC). The 3D-GAN is used to transform the acquired DE-CTC volumes into arepresentative cleansed CTC volume by use of an iterative self-supervised method that adapts the scheme to the uniqueconditions of each case. Our preliminary evaluation with an anthropomorphic phantom indicated that the use of the 3DGANEC scheme with DE-CTC features and the self-supervised scheme generates EC images of higher quality than thoseobtained by use of SE-CTC or conventional training samples only.
展开▼
