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

August 12, 2026pubmed logopapers

Authors

Steffani S,Piscione M,Gaudio D,Meghnagi G,Montella V,Fiorini F,Micillo A,Tagliati C,Asmundo L,Manenti G,Chiocchi M,Laudazi M

Affiliations (7)

  • Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.
  • Department of Cardiology, SS. Annunziata Hospital, ASL2, 66100 Chieti, Italy.
  • Fondazione Policlinico Campus Bio-Medico, University of Rome, Via Alvaro del Portillo 200, 00128 Rome, Italy.
  • Diagnostic Imaging Unit, Casa di Cura Villa Delle Querce, 00040 Nemi, Italy.
  • AST Ancona, Ospedale di Comunità Maria Montessori di Chiaravalle, Via Fratelli Rosselli 176, 60033 Chiaravalle, Italy.
  • Department of Radiology, Ospedale Niguarda Ca' Granda, Piazza Ospedale Maggiore, 20162 Milan, Italy.
  • Diagnostic Imaging Department, AOU Policlinico Tor Vergata, 00133 Rome, Italy.

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.

Topics

Journal ArticleReview

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