Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks.

ABSTRACT: Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar. Methods: A dataset of LGE MRI

SUBMITTER: O'Brien H 

PROVIDER: S-EPMC8283258 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature