Phase identification for dynamic CT enhancements with generative adversarial network.

ABSTRACT:

Purpose

Dynamic contrast-enhanced computed tomography (CT) is widely used to provide dynamic tissue contrast for diagnostic investigation and vascular identification. However, the phase information of contrast injection is typically recorded manually by technicians, which introduces missing or mislabeling. Hence, imaging-based contrast phase identification is appealing, but challenging, due to large variations among different contrast protocols, vascular dynamics, and metabolism, especially for clinically acquired CT scans. The purpose of this study is to perform imaging-based phase identification for dynamic abdominal CT using a proposed adversarial learning framework across five representative contrast phases.

Methods

A generative adversarial network (GAN) is proposed as a

SUBMITTER: Tang Y 

PROVIDER: S-EPMC9053868 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature