Leveraging CycleGAN in Lung CT Sinogram-free Kernel Conversion

Cancer screening guidelines recommend annual screening with low-dose Computed Tomography (CT) for high-risk groups to reduce lung cancer mortality. Unfortunately, lung CT effectiveness can be strongly impacted by the considered reconstruction kernel. This selection is (almost) final, implying that it is no longer possible to change the used reconstruction kernel once applied, unless a sinogram for the conversion is available. The aim of this paper was to introduce a new sinogram-free kernel conversion in the contest of lung CT imaging. In particular, we wanted to define a procedure able to deal with different acquisition protocols, able to be used in an unpaired images scenario. To this aim, we leveraged a CycleGAN, considering the CT kernel conversion task as a style transfer problem. Results show that the CT kernel conversion can be effectively addressed as a style transfer problem.