  Low Dose Computed Tomography (LDCT) is clinically desirable due to the
reduced radiation to patients. However, the quality of LDCT images is often
sub-optimal because of the inevitable strong quantum noise. Inspired by their
unprecedent success in computer vision, deep learning (DL)-based techniques
have been used for LDCT denoising. Despite the promising noise removal ability
of DL models, people have observed that the resolution of the DL-denoised
images is compromised, decreasing their clinical value. Aiming at relieving
this problem, in this work, we developed a more effective denoiser by
introducing a high-resolution network (HRNet). Since HRNet consists of multiple
branches of subnetworks to extract multiscale features which are later fused
together, the quality of the generated features can be substantially enhanced,
leading to improved denoising performance. Experimental results demonstrated
that the introduced HRNet-based denoiser outperforms the benchmarked UNet-based
denoiser in terms of superior image resolution preservation ability while
comparable, if not better, noise suppression ability. Quantitative metrics in
terms of root-mean-squared-errors (RMSE)/structure similarity index (SSIM)
showed that the HRNet-based denoiser can improve the values from 113.80/0.550
(LDCT) to 55.24/0.745 (HRNet), in comparison to 59.87/0.712 for the UNet-based
denoiser.
