Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Simona Turco, Peter Frinking, Rogier Wildeboer, Marcel Arditi, Hessel Wijkstra, Jonathan R. Lindner, Massimo Mischi

    Research output: Contribution to journalReview articlepeer-review

    1 Scopus citations

    Abstract

    Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

    Original languageEnglish (US)
    Pages (from-to)518-543
    Number of pages26
    JournalUltrasound in Medicine and Biology
    Volume46
    Issue number3
    DOIs
    StatePublished - Mar 2020

    Keywords

    • Contrast-enhanced ultrasound
    • Indicator dilution theory
    • Kinetic modeling
    • Machine learning
    • Molecular ultrasound
    • Multiparametric ultrasound
    • Quantitative ultrasound
    • Spatiotemporal analysis
    • Time–intensity curves
    • Ultrasound contrast agents

    ASJC Scopus subject areas

    • Biophysics
    • Radiological and Ultrasound Technology
    • Acoustics and Ultrasonics

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