English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

While machine learning approaches perform well on their training domain, theygenerally tend to fail in a real-world application. In cardiovascular magneticresonance imaging (CMR), respiratory motion represents a major challenge interms of acquisition quality and therefore subsequent analysis and finaldiagnosis. We present a workflow which predicts a severity score forrespiratory motion in CMR for the CMRxMotion challenge 2022. This is animportant tool for technicians to immediately provide feedback on the CMRquality during acquisition, as poor-quality images can directly be re-acquiredwhile the patient is still available in the vicinity. Thus, our method ensuresthat the acquired CMR holds up to a specific quality standard before it is usedfor further diagnosis. Therefore, it enables an efficient base for properdiagnosis without having time and cost-intensive re-acquisitions in cases ofsevere motion artefacts. Combined with our segmentation model, this can helpcardiologists and technicians in their daily routine by providing a completepipeline to guarantee proper quality assessment and genuine segmentations forcardiovascular scans. The code base is available athttps://github.com/MECLabTUDA/QA_med_data/tree/dev_QA_CMRxMotion.

Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation