  Computed Tomography (CT) imposes risk on the patients due to its inherent
X-ray radiation, stimulating the development of low-dose CT (LDCT) imaging
methods. Lowering the radiation dose reduces the health risks but leads to
noisier measurements, which decreases the tissue contrast and causes artifacts
in CT images. Ultimately, these issues could affect the perception of medical
personnel and could cause misdiagnosis. Modern deep learning noise suppression
methods alleviate the challenge but require low-noise-high-noise CT image pairs
for training, rarely collected in regular clinical workflows. In this work, we
introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM
that can be trained without the high-dose CT projection ground truth images.
Unlike previously proposed self-supervised techniques, the introduced method
exploits the connections between the adjacent projections and the actual model
of CT noise distribution. Such a combination allows for interpretable
no-reference denoising using nothing but the original noisy LDCT projections.
Our experiments with LDCT data demonstrate that the proposed method reaches the
level of the fully supervised models, sometimes superseding them, easily
generalizes to various noise levels, and outperforms state-of-the-art
self-supervised denoising algorithms.
