  X-ray computed tomography (CT) uses different filter kernels to highlight
different structures. Since the raw sinogram data is usually removed after the
reconstruction, in case there are additional need for other types of kernel
images that were not previously generated, the patient may need to be scanned
again. Accordingly, there exists increasing demand for post-hoc image domain
conversion from one kernel to another without sacrificing the image quality. In
this paper, we propose a novel unsupervised continuous kernel conversion method
using cycle-consistent generative adversarial network (cycleGAN) with adaptive
instance normalization (AdaIN). Even without paired training data, not only can
our network translate the images between two different kernels, but it can also
convert images along the interpolation path between the two kernel domains. We
also show that the quality of generated images can be further improved if
intermediate kernel domain images are available. Experimental results confirm
that our method not only enables accurate kernel conversion that is comparable
to supervised learning methods, but also generates intermediate kernel images
in the unseen domain that are useful for hypopharyngeal cancer diagnosis.
