Key result
Automated post-processing of clinical late gadolinium enhancement images using graph cut algorithms significantly reduces analysis time to less than one second per slice with no observer variability.
Automated post-processing of LGE CMR images for infarct quantification shows promise in reducing analysis time and observer variability, though technical challenges remain.
Cardiovascular imaging techniques have advanced our understanding of the pathophysiology of acute and chronic myocardial infarction (MI). Infarct size is intimately related to adverse LV remodeling, heart failure and clinical outcomes (1,2). Rapid, robust, and reproducible quantification of infarct size is therefore desirable in both clinical and research settings. The late gadolinium enhancement (LGE) technique using cardiovascular magnetic resonance is the gold standard method because of its high spatial resolution and excellent contrast (3). The technique uses a chelated gadolinium contrast agent, which acts as an extracellular tracer. This accumulates in areas where cell membranes are not intact (cells destined to die in acute MI) or where there is replacement fibrosis (chronic MI). Gadolinium, which shortens T1, causes the infarct to appear bright (white) on a T1 weighted image (4). Infarction imaged in this way correlates accurately with histological specimens in ex vivo animal studies (3,5) and is prognostic in multiple human studies (1). It guides therapy and is used as a surrogate endpoint in many acute infarct trials.
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White et al. (2013) conducted an editorial in Myocardial infarction. Automated scar quantification (graph cut algorithms) vs. Manual or semi-automated methods (FWHM) was evaluated. Automated post-processing of clinical late gadolinium enhancement images using graph cut algorithms significantly reduces analysis time to less than one second per slice with no observer variability.
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