  Cardiovascular magnetic resonance (CMR) imaging has become a modality with
superior power for the diagnosis and prognosis of cardiovascular diseases. One
of the essential basic quality controls of CMR images is to investigate the
complete cardiac coverage, which is necessary for the volumetric and functional
assessment. This study examines the full cardiac coverage using a 3D
convolutional model and then reduces the number of false predictions using an
innovative salient region detection model. Salient regions are extracted from
the short-axis cine CMR stacks using a three-step proposed algorithm. Combining
the 3D CNN baseline model with the proposed salient region detection model
provides a cascade detector that can reduce the number of false negatives of
the baseline model. The results obtained on the images of over 6,200
participants of the UK Biobank population cohort study show the superiority of
the proposed model over the previous state-of-the-art studies. The dataset is
the largest regarding the number of participants to control the cardiac
coverage. The accuracy of the baseline model in identifying the
presence/absence of basal/apical slices is 96.25\% and 94.51\%, respectively,
which increases to 96.88\% and 95.72\% after improving using the proposed
salient region detection model. Using the salient region detection model by
forcing the baseline model to focus on the most informative areas of the images
can help the model correct misclassified samples' predictions. The proposed
fully automated model's performance indicates that this model can be used in
image quality control in population cohort datasets and also real-time
post-imaging quality assessments.
