  In the intention of minimizing excessive X-ray radiation administration to
patients, low-dose computed tomography (LDCT) has become a distinct trend in
radiology. However, while lowering the radiation dose reduces the risk to the
patient, it also increases noise and artifacts, compromising image quality and
clinical diagnosis. In most supervised learning methods, paired CT images are
required, but such images are unlikely to be available in the clinic. We
present a self-supervised learning model (Noise2Projection) that fully exploits
the raw projection images to reduce noise and improve the quality of
reconstructed LDCT images. Unlike existing self-supervised algorithms, the
proposed method only requires noisy CT projection images and reduces noise by
exploiting the correlation between nearby projection images. We trained and
tested the model using clinical data and the quantitative and qualitative
results suggest that our model can effectively reduce LDCT image noise while
also drastically removing artifacts in LDCT images.
