Computed Tomography Derived Extracellular Volume (CT-ECV): Assessment, Protocols and Utility
Year: 2025
Type: article
Abstract: Purpose of Review This review examines recent developments in computed tomography-derived extracellular volume (CT-ECV) assessment, focusing on acquisition protocols, clinical applications, and emerging technologies. Recent Findings CT-derived extracellular volume fraction (CT-ECV) has emerged as a robust tool for myocardial characterisation across various cardiac conditions. Recent advancements in technology, including photon-counting detector CT and artificial intelligence integration, have enhanced image quality and workflow efficiency while reducing radiation exposure. CT-ECV demonstrates particular utility in cardiac amyloidosis detection, cancer therapy-related cardiac dysfunction monitoring, and risk stratification in aortic stenosis. The technique boosts the utility of CCTA by adding myocardial tissue characterisation to coronary evaluation. Synthetic ECV calculation and automated post-processing further streamline clinical implementation. While protocol standardisation remains an important goal, CT-ECV’s ability to provide valuable prognostic information positions it as an increasingly important tool in cardiovascular imaging. As evidence accumulates supporting its clinical value, CT-ECV is likely to play a growing role in patient care and research. Summary CT-ECV has matured into a versatile and powerful tool for myocardial characterisation across a spectrum of cardiac conditions. The integration of cutting-edge technologies, such as photon-counting detector CT and artificial intelligence, has significantly enhanced image quality and streamlined workflow efficiency while simultaneously reducing radiation exposure. This evolution addresses previous limitations and expands the clinical applicability of CT-ECV.
Authors: Sarang Paleri, Clare Rayner, Timothy Abrahams, Sheran Vasanthakumar, Vishal Goel, Rahul Muthalaly, Andrew Lin, Nitesh Nerlekar
Institutions: Victorian Heart Hospital, Monash University, Royal Hobart Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,890
Funder: Monash University
OpenAlex ID: W4411458441
