Improving image quality and lung nodule detection for low-dose chest CT by using generative adversarial network reconstruction.

ABSTRACT:

Objectives

To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT).

Methods

In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability.

Results

For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res mo

SUBMITTER: Cao Q 

PROVIDER: S-EPMC9815729 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature