  The resurgence of deep neural networks has created an alternative pathway for
low-dose computed tomography denoising by learning a nonlinear transformation
function between low-dose CT (LDCT) and normal-dose CT (NDCT) image pairs.
However, those paired LDCT and NDCT images are rarely available in the clinical
environment, making deep neural network deployment infeasible. This study
proposes a novel method for self-supervised low-dose CT denoising to alleviate
the requirement of paired LDCT and NDCT images. Specifically, we have trained
an invertible neural network to minimize the pixel-based mean square distance
between a noisy slice and the average of its two immediate adjacent noisy
slices. We have shown the aforementioned is similar to training a neural
network to minimize the distance between clean NDCT and noisy LDCT image pairs.
Again, during the reverse mapping of the invertible network, the output image
is mapped to the original input image, similar to cycle consistency loss.
Finally, the trained invertible network's forward mapping is used for denoising
LDCT images. Extensive experiments on two publicly available datasets showed
that our method performs favourably against other existing unsupervised
methods.
