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Diagnostic Value and Operational Recommendations for Late Iodine Enhancement and ECV Quantification in Single-Energy Computed Tomography: A Narrative Review

Aug 2026 · Diagnostics · Vol 16, pp. 2547 · 0 citations · 74 references
Medicine

TL;DR

Optimize SECT through technical adjustments and standardized training is crucial for integrating myocardial characterization into routine workflows and demonstrates diagnostic and prognostic utility in conditions like acute myocardial infarction, hypertrophic cardiomyopathy, cardiac amyloidosis, and left ventricular thrombus detection.

Abstract

While traditionally focused on coronary anatomy, cardiac computed tomography now enables non-invasive myocardial tissue characterization. By evaluating late iodine enhancement (LIE) and extracellular volume (ECV), single-energy CT (SECT) provides a valuable alternative to cardiac magnetic resonance for assessing ischemic and non-ischemic pathologies. However, clinical implementation of SECT faces technical challenges, primarily the low contrast-to-noise ratio (CNR) of iodine and the reliance on image subtraction for ECV quantification, both of which increase radiation exposure and susceptibility to spatial misregistration. To address these issues, protocol optimization is essential. Evidence-based recommendations include using low tube voltages to shift the X-ray spectrum closer to the iodine K-edge, paired with high reference tube currents. Additionally, delayed acquisition timing should be tailored to specific pathological targets to account for differences in contrast kinetics, and advanced iterative or deep learning image reconstructions should be implemented to mitigate noise. Optimized SECT demonstrates diagnostic and prognostic utility in conditions like acute myocardial infarction, hypertrophic cardiomyopathy, cardiac amyloidosis, and left ventricular thrombus detection. While spectral imaging represents the future, optimizing SECT through technical adjustments and standardized training is crucial for integrating myocardial characterization into routine workflows.

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