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

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.

Acoustic characterization of tissue-mimicking materials for ultrasound perfusion imaging research

“…Several advanced techniques have been proposed for quantification of perfusion in a broad range of clinical applications, especially in cardiology, radiology, and oncology. Quantification methods are often based on empirical or physics-driven modeling of the UCA flow kinetics, leading to the estimation of parameters related to blood flow and velocity (Mischi et al, 2018;Turco et al, 2020). However, although in-vivo studies have shown promising results (Mischi et al, 2012;Feinstein, 2004;van Sloun et al, 2017;Kuenen et al, 2011;Wildeboer et al, 2018), the relationship between the underlying (micro)vascular architecture and the UCA kinetics, along with the resulting CEUS image enhancement and ultimately the estimated hemodynamic parameters is not well understood.…”

Artificial intelligence in medical ultrasonography: driving on an unpaved road

“…The more complex the image acquisition process is, the more difficult it may be to apply AI tools. Further research will likely focus on an easily approachable organ with application of elastography, color Doppler ultrasound, and contrast enhanced Doppler ultrasound (14). 3D acquisition of anatomy has the potential to expand efficacy of AI application.…”

Prostate Cancer Ultrasound: Is Still a Valid Tool?

“…By contrast, in fact, there are far fewer adverse reactions than gadolinium and/or iodine contrast media. Moreover, if we consider that the gas-filled microbubbles do not reach the renal district and the urinary tract, it is clear how the problem of the potential nephrotoxicity of contrast media passes in the background: they can be used regardless of the kidney function of patients and/or the presence of urinary tract obstruction [20]. This ultrasound contrast medium property finds an additional point in its favor since, being a pure blood pool agent, it directly reaches the vascular district without spreading into the interstice of the surrounding tissues, element that could obscure the micro-vascularization, thus, providing a better evaluation of blood microflows than CDUS [21].…”