  Flow analysis carried out using phase contrast cardiac magnetic resonance
imaging (PC-CMR) enables the quantification of important parameters that are
used in the assessment of cardiovascular function. An essential part of this
analysis is the identification of the correct CMR views and quality control
(QC) to detect artefacts that could affect the flow quantification. We propose
a novel deep learning based framework for the fully-automated analysis of flow
from full CMR scans that first carries out these view selection and QC steps
using two sequential convolutional neural networks, followed by automatic aorta
and pulmonary artery segmentation to enable the quantification of key flow
parameters. Accuracy values of 0.958 and 0.914 were obtained for view
classification and QC, respectively. For segmentation, Dice scores were
$>$0.969 and the Bland-Altman plots indicated excellent agreement between
manual and automatic peak flow values. In addition, we tested our pipeline on
an external validation data set, with results indicating good robustness of the
pipeline. This work was carried out using multivendor clinical data consisting
of 986 cases, indicating the potential for the use of this pipeline in a
clinical setting.
