  In coronary CT angiography, a series of CT images are taken at different
levels of radiation dose during the examination. Although this reduces the
total radiation dose, the image quality during the low-dose phases is
significantly degraded. To address this problem, here we propose a novel
semi-supervised learning technique that can remove the noises of the CT images
obtained in the low-dose phases by learning from the CT images in the routine
dose phases. Although a supervised learning approach is not possible due to the
differences in the underlying heart structure in two phases, the images in the
two phases are closely related so that we propose a cycle-consistent
adversarial denoising network to learn the non-degenerate mapping between the
low and high dose cardiac phases. Experimental results showed that the proposed
method effectively reduces the noise in the low-dose CT image while the
preserving detailed texture and edge information. Moreover, thanks to the
cyclic consistency and identity loss, the proposed network does not create any
artificial features that are not present in the input images. Visual grading
and quality evaluation also confirm that the proposed method provides
significant improvement in diagnostic quality.
