DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data. (arXiv:2211.01293v1 [eess.IV])

Magnetic resonance (MR) and computer tomography (CT) images are two typical
types of medical images that provide mutually-complementary information for
accurate clinical diagnosis and treatment. However, obtaining both images may
be limited due to some considerations such as cost, radiation dose and modality
missing. Recently, medical image synthesis has aroused gaining research
interest to cope with this limitation. In this paper, we propose a
bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN),
to synthesis medical images from unpaired data. …

arxiv cyclegan data