Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

ABSTRACT:

Background

Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders.

Purpose

To develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification.

Study type

Retrospective.

Population

A total of 191 hypertrophic cardiomyopathy patients: 1) 162 patients from two sites randomly split into training (50%; 81 patients), validation (25%, 40 patients), and testing (25%; 41 patients); and 2) an external testing dataset (29 patients) from a th

SUBMITTER: Fahmy AS 

PROVIDER: S-EPMC8359184 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature