Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network.

ABSTRACT:

Objectives

The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.

Methods

In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features,

SUBMITTER: Shiri I 

PROVIDER: S-EPMC7467843 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature