Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

ABSTRACT:

Background

Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol.

Methods

To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the

SUBMITTER: Xia Z 

PROVIDER: S-EPMC10423351 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature