Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Abstract

Background: Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images. Methods: In this light, 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, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body. Results: 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 and − 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% and median filter with RMSE = 129.60 ± 18.09 HU and bias = −6.15% ± 2.24%. Conclusions: The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.