Keywords: Diagnosis/Prediction, Cardiomyopathy, Cine, LGE, Hypertrophic cardiomyopathy, RegGAN, CBAM
Motivation: LGE CMR, the standard clinical non-invasive characterization, is limited by its reliance on intravenous contrast agents and long waiting time. Therefore, developing a contrast agent-free technology is essential for achieving fast and cost-effective CMR scans.
Goal(s): To evaluate the reproducibility and reliability of the virtual LGE based on Cine, and compare it with native LGE to assess its efficiency in diagnosing HCM in clinical context.
Approach: RegGAN was employed to forecast LGE imaging and rectify the outcomes. And CBAM was utilized to quantify the influence of diverse components in LGE.
Results: RegGAN-CBAM demonstrates favorable performance in both image and enhancement prediction of LGE.
Impact: The performance of virtual LGE based on Cine exhibits a notable level of diagnostic efficiency and reliability. This approach serves as a non-invasive myocardial tissue characterization method with practical applicability in the clinical assessment of HCM.
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