  Tissue characterisation with 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.
Convolutional neural networks 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 ShMOLLI T1 mapping at 1.5T using a Probabilistic Hierarchical
Segmentation (PHiSeg) network. In addition, we use the uncertainty information
provided by the PHiSeg network in a novel automated quality control (QC) step
to identify uncertain T1 values. The PHiSeg network and QC were validated
against manual analysis on a cohort of the UK Biobank containing healthy
subjects and chronic cardiomyopathy patients. We used the proposed method to
obtain reference T1 ranges for the left ventricular myocardium in healthy
subjects as well as common clinical cardiac conditions. T1 values computed from
automatic and manual segmentations were highly correlated (r=0.97).
Bland-Altman analysis showed good agreement between the automated and manual
measurements. The average Dice metric was 0.84 for the left ventricular
myocardium. The sensitivity of detection of erroneous outputs was 91%. Finally,
T1 values were automatically derived from 14,683 CMR exams from the UK Biobank.
The proposed pipeline allows for automatic analysis of myocardial native T1
mapping and includes a QC process to detect potentially erroneous results. T1
reference values were presented for healthy subjects and common clinical
cardiac conditions from the largest cohort to date using T1-mapping images.
