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

摘要

Cardiovascular magnetic resonance (CMR) provides insights into myocardial structure and function noninvasively, with high diagnostic accuracy and without ionising radiation. Late Gadolinium Enhancement (LGE) has become the reference standard for non-invasive imaging of myocardial scar and focal fibrosis in both ischaemic [2] and non-ischaemic cardiomyopathy [3]. LGE is useful in cardiac conditions which have stark regional differences within the myocardium, but it cannot correctly visualise myocardial pathology that is diffuse in nature and affects the myocardium uniformly. Examples include diffuse myocardial inflammation, fibrosis, hypertrophy, and infiltration [4]. In contrast, native T1-mapping provides quantitative myocardial tissue characterisation, without the need for gadolinium [5]. Previous work has shown that T1 mapping can help to detect diffuse myocardial disease in early disease stages and aids in diagnosing the diseases’ underlying cardiac dysfunction.