  We set out to simulate four reduced dose-levels (60%-dose, 40%-dose,
20%-dose, and 10%-dose) of standard CT imaging using Beer-Lambert's law across
49 patients infected with COVID-19. Then, three denoising filters, namely
Gaussian, Bilateral, and Median, were applied to the different low-dose CT
images, the quality of which was assessed prior to and after the application of
the various filters via calculation of peak signal-to-noise ratio (PSNR), root
mean square error (RMSE), structural similarity index measure (SSIM), and
relative CT-value bias, separately for the lung tissue and whole-body. The
quantitative evaluation indicated that 10%-dose CT images have inferior quality
(with RMSE=322.1-+104.0 HU and bias=11.44-+4.49% in the lung) even after the
application of the denoising filters. The bilateral filter exhibited superior
performance to suppress the noise and recover the underlying signals in
low-dose CT images compared to the other denoising techniques. The bilateral
filter led to RMSE and bias of 100.21-+16.47 HU, -0.21-+1.20%, respectively in
the lung regions for 20%-dose CT images compared to the Gaussian filter with
RMSE=103.46-+15.70 HU and bias=1.02-+1.68%, median filter with
RMSE=129.60-+18.09 HU and bias=-6.15-+2.24%, and the nonfiltered 20%-dose CT
with RMSE=217.37-+64.66 HU and bias=4.30-+1.85%. In conclusion, the 20%-dose CT
imaging followed by the bilateral filtering introduced a reasonable compromise
between image quality and patient dose reduction.
