Table 1 Machine learning and deep learning for LGE quantification and parametric mapping
From: Machine learning in cardiovascular magnetic resonance: basic concepts and applications
Author | Myocardial disease | Image substrate | Application |
|---|---|---|---|
Fahmy et al., 2018 (Ref. 51) | HCM | LGE | Delineate and quantify scar volume in patients with HCM |
Hann et al., 2018 (Ref 52) | Â | T1 mapping | Automated LV segmentation of T1 maps using a ShMOLLI sequence in order to speed up LGE quantification based on T1 mapping |
Fahmy et al., 2019 (Ref 53) | Various diseases | T1 mapping | DL based image analysis and motion correction for myocardial T1 mapping to provide fast and automated T1 mapping analysis (DICE: 0.85) |
Farrag et al., 2019 (Ref 55) | Myocardial infarction | T1 mapping and CINE | DL based automated LV segmentation of T1 maps using a ShMOLLI sequence (DICE: 0.84) |
Martini et al., 2018 (Ref 54) | Various diseases | T1 mapping | Automated segmental analysis of T1 maps (DICE: 0.98, Jaccard: 0.97) |