TY - JOUR
T1 - Contrast-enhanced ultrasound quantification
T2 - from kinetic modeling to machine learning
AU - Turco, Simona
AU - Frinking, Peter
AU - Wildeboer, Rogier
AU - Arditi, Marcel
AU - Wijkstra, Hessel
AU - Lindner, Jonathan R
AU - Mischi, Massimo
N1 - Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Contrast-enhanced ultrasound
KW - Indicator dilution theory
KW - Kinetic modeling
KW - Machine learning
KW - Molecular ultrasound
KW - Multiparametric ultrasound
KW - Quantitative ultrasound
KW - Spatiotemporal analysis
KW - Time–intensity curves
KW - Ultrasound contrast agents
KW - Time-intensity curves
UR - http://www.scopus.com/inward/record.url?scp=85077655323&partnerID=8YFLogxK
U2 - 10.1016/j.ultrasmedbio.2019.11.008
DO - 10.1016/j.ultrasmedbio.2019.11.008
M3 - Review article
C2 - 31924424
VL - 46
SP - 518
EP - 543
JO - Ultrasound in Medicine and Biology
JF - Ultrasound in Medicine and Biology
SN - 0301-5629
IS - 3
ER -