Automated quantification of myocardial tissue characteristics from native T₁ mapping using neural networks with uncertainty-based quality-control.

ABSTRACT:

Background

Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T₁ mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies.

Methods

Convolutional neural networks (CNNs) with Bayesian inference are a category of artificial neural networks which model the uncertainty of the network output. This study presents an automated framework for tissue characterisation from native shortened modified Look-Locker inversion recovery ShMOLLI T₁ mapping at 1.5 T usin

SUBMITTER: Puyol-Anton E 

PROVIDER: S-EPMC7439533 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature