Keywords: Myocardium, Radiomics, Late Gadolinium Enhancement; Myocardial Infarction; Microvascular Obstruction; Random Forest;
Motivation: Late gadolinium Enhancement (LGE) is the method of choice for assessing myocardial infarction (MI) and viability, essential to guide revascularization decisions. LGE also shows no-reflow regions that occur when blood flow remains inadequate post-revascularization. Existing automatic segmentation methods identify regions of LGE uptake but ignore no-reflow regions.
Goal(s): Our goal is to provide a robust and automated solution for the detection of MI and no-reflow.
Approach: We propose two LGE-based radiomics models, RAMI and RAMI-NOR, to improve MI diagnosis and detect no-reflow, respectively.
Results: RAMI distinguishes normal and pathological cases accurately. RAMI-NOR shows potential in assessing MI with no-reflow but needs further refinement.
Impact: The proposed RAMI and RAMI-NOR methods extract radiomics features from LGE images to automatically detect infarcted and microvascular obstruction areas, essential for the diagnosis and treatment management of patients suffering from myocardial infarction.
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