  The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends
on the ability of the operator to correctly tune the acquisition parameters to
the subject being scanned and on the potential occurrence of imaging artefacts
such as cardiac and respiratory motion. In the clinical practice, a quality
control step is performed by visual assessment of the acquired images: however,
this procedure is strongly operator-dependent, cumbersome and sometimes
incompatible with the time constraints in clinical settings and large-scale
studies. We propose a fast, fully-automated, learning-based quality control
pipeline for CMR images, specifically for short-axis image stacks. Our pipeline
performs three important quality checks: 1) heart coverage estimation, 2)
inter-slice motion detection, 3) image contrast estimation in the cardiac
region. The pipeline uses a hybrid decision forest method - integrating both
regression and structured classification models - to extract landmarks as well
as probabilistic segmentation maps from both long- and short-axis images as a
basis to perform the quality checks. The technique was tested on up to 3000
cases from the UK Biobank as well as on 100 cases from the UK Digital Heart
Project, and validated against manual annotations and visual inspections
performed by expert interpreters. The results show the capability of the
proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK
Biobank, sensitivity and specificity respectively 88% and 99% for heart
coverage estimation, 85% and 95% for motion detection), allowing their
exclusion from the analysed dataset or the triggering of a new acquisition.
