Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

ABSTRACT: Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targe

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC7718111 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature