  While machine learning approaches perform well on their training domain, they
generally tend to fail in a real-world application. In cardiovascular magnetic
resonance imaging (CMR), respiratory motion represents a major challenge in
terms of acquisition quality and therefore subsequent analysis and final
diagnosis. We present a workflow which predicts a severity score for
respiratory motion in CMR for the CMRxMotion challenge 2022. This is an
important tool for technicians to immediately provide feedback on the CMR
quality during acquisition, as poor-quality images can directly be re-acquired
while the patient is still available in the vicinity. Thus, our method ensures
that the acquired CMR holds up to a specific quality standard before it is used
for further diagnosis. Therefore, it enables an efficient base for proper
diagnosis without having time and cost-intensive re-acquisitions in cases of
severe motion artefacts. Combined with our segmentation model, this can help
cardiologists and technicians in their daily routine by providing a complete
pipeline to guarantee proper quality assessment and genuine segmentations for
cardiovascular scans. The code base is available at
https://github.com/MECLabTUDA/QA_med_data/tree/dev_QA_CMRxMotion.
