Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network.

ABSTRACT: Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This mode

SUBMITTER: Lyu Q 

PROVIDER: S-EPMC8376223 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature