An appropriate threshold for LGE images using deep learning-based reconstruction in revelation clinically unrecognized myocardial infarction

Motivation: To precisely screen out infarction in patients with unrecognized myocardial infarction (UMI) in hope of early intervention to reduce adverse cardiac events. Goal(s): To evaluate deep learning reconstruction based late gadolinium enhancement (LGEDL) in comparison with conventional reconstructed LGE (LGEO) and also to explore an appropriate threshold method for LGE measurements. Approach: LGEDL and LGEO of 77 patients diagnosed with UMI were evaluated for image quality and analyzed for MI areas using different standard deviation thresholds and a full-width-half-height (FWHM) method. Results: The STRM ≥ 4SD and ≥ 3SD is respectively reckoned as the best reference threshold for LGEDL and LGEO. Impact: The deep-learning based reconstruction LGE images had better image quality and reliable pathological evidences for detection of UMI. Significantly different Parea using threshold techniques for LGEDL and LGEO indicated the utility of STRM should be concerned.