Ultrasound Contrast Imaging: Fundamentals and Emerging Technology

The development of microbubble contrast agents has broadened the scope of medical ultrasound imaging. Along with dedicated imaging techniques, these agents provide enhanced echoes from the blood pool and have enabled diagnostic ultrasound to assess and quantify microvascular blood flow. Contrast-enhanced ultrasound is currently used worldwide with clinical indications in cardiology and radiology, and it continues to evolve and develop through innovative technological advancements. In this review article, we present an overview of the basic microbubble physics and bubble-specific imaging techniques that enable this modality, and follow this with a discussion on new and emerging applications.

1 Introduction

Ultrasound imaging is a well-established clinical tool for the morphological assessment of soft tissues, employed frequently in obstetrics, cardiology, and radiology [1]. As an ultrasonic wave (which is a longitudinal wave) is transmitted into the body, reflections are generated from tissue interfaces that are characterized by different acoustic properties, i.e., speed of sound and density. These scattered signals are recorded by the same transmitting transducer and used to generate an image. At typical diagnostic frequencies ($\approx$1–10 MHz), the intrinsic scattering from the blood pool, however, is typically several orders of magnitude lower than tissue due to the size and properties of red blood cells [2]. Consequently, blood appears dark on conventional ultrasound images and blood flow characteristics cannot be readily assessed. For larger vessels, the relative motion of red blood cells compared to the surrounding tissue can be exploited to assess blood velocity using Doppler techniques [3], a strategy employed in many clinical applications (e.g., obstetrics [4], assessment of peripheral artery disease [5], cardiology [6]). This technique has limitations however when dealing with regions of slow blood flow, large tissue motion and/or low hematocrit percentage [1, 7].

Ultrasound contrast agents comprise of a suspension of small spheres of gas with a low solubility in blood (e.g., perfluorocarbon), typically ranging in size from below 1 to 8 µm in diameter. Unlike contrast agents used in other modalities, such as MRI and CT, the relatively large size of ultrasound contrast agents ensures that they remain strictly intravascular and act as red blood cell tracers [8]. Due to the compressibility of their gas cores, microbubbles vibrate about their equilibrium radius in an ultrasound field and possess scattering cross-sections several orders of magnitude higher than a solid particle of the same size [9]. The bubbles are stabilized by a thin bio-compatible encapsulation layer—typically a phospholipid monolayer, to offer a sufficient compromise between bubble vibration flexibility and resistance to dissolution in-vivo over timescales relevant for imaging, e.g., half-lives of minutes [10, 11].

Microbubble suspensions, typically on the order of ${10}^9$ bubbles/ml, are injected intravenously into a peripheral vein in the arm [8], with a whole-body dose ranging from 0.2 to 2 ml [12]. There have been millions of diagnostic injections of contrast agent microbubbles worldwide [12], and they are accompanied by an excellent safety profile. Recent meta-analysis surveying microbubble tolerance indicates that the dominant cause of severe adverse effects is pseudoanaphylaxis (CARPA), with an estimated rate on the order of 0.004%–0.009% [13]. This rate is comparable to most analgesics and antibodies (0.005%–0.015% [14]), and similar if not lower than for other contrast imaging agents, e.g., CT with a rate of 0.04% [15], MR with a rate of 0.002%–0.005% [16, 17]. Table 1 lists the clinical contrast agents, along with details on their salient characteristics and clinically approved applications. Microbubbles are approved in over 70 countries, predominately for cardiac applications, whereby their strong echo signal in the heart chambers improves left ventricular opacification (LVO). Recently, Lumason™ was approved for liver imaging and in various pediatric applications [18]. Aside from the clinical uses listed here, microbubbles are currently in use worldwide in many off label clinical imaging applications, including assessment of microvascular perfusion (e.g., myocardial [19], angiogenesis imaging [20]), imaging of the carotid to assess vascular stenosis [21] and plaque stability [22], lesion and flow characteristics in the abdominal region [23, 24], breast lesion detection [25], evaluation of inflammatory bowel disease [26], and assessment of ovaries [27], prostate [28] and thyroid [29].

TABLE 1

TABLE 1. Current clinical contrast agent microbubbles, their salient characteristics, and their approved uses.

In this review, we present an overview of this established yet evolving imaging modality. First, we present a brief summary of the fundamental physics of microbubble behaviors that are critical for the effectiveness of this approach, followed by an introduction to the main conventional pulse sequences that are designed to exploit these behaviors to generate bubble-specific images. Next, we discuss exciting advancements in the techniques and applications of ultrasound contrast imaging, including the development of emerging contrast agents, novel imaging and image analysis techniques, and the implementation of contrast ultrasound as a therapy monitoring technique. Note that this is not a comprehensive review, rather an overview of the critical work that has defined this modality and salient investigations into new and ground-breaking applications.

2 Ultrasound-Microbubble Interactions

A gas-filled microbubble vibrates when traversing through an acoustic beam, contracting and expanding about its equilibrium radius $R_0$. Almost all the current models that explain the oscillation dynamics of a bubble have their origin in Rayleigh-Plesset-type equations [30], which describe the radial motion of an isolated, unencapsulated bubble. This equation, which only incorporates spherical vibrations, can be derived by applying Newton’s third law to the surface of a bubble and equilibrating the pressure on the bubble wall from the gas inside and the surrounding fluid media outside, resulting in the following equation:

$\text{R}\ddot{\text{R}}+\frac{3}{2}{\dot{R}}^2=\frac{1}{\text{ρ}}\left[{\text{P}}_{\text{G}0}{\left(\frac{{\text{R}}_0}{\text{R}}\right)}^{3\text{γ}}+{\text{P}}_{\text{v}}-\frac{2\text{σ}}{\text{R}}-4{\text{η}}_{\text{L}}\frac{\dot{\text{R}}}{\text{R}}-{\text{P}}_0-{\text{P}}_{\text{ac}}\left(\text{t}\right)\right],(1)$

where $R$ is the radius of the bubble, $\text{ρ}$ is the density of the liquid, $P_{G0}=P_0-P_v+2\sigma /R_0$ is the pressure inside the bubble with $P_0$ the atmospheric pressure, $P_v$ the vapor pressure inside the bubble and $\text{σ}$ is the surface tension at the gas-liquid interface, $\text{γ}$ is the polytropic exponent of the gas; ${\eta }_L$ is the dynamic viscosity of the liquid; $P_{ac}$ is the driving acoustic pressure due to the ultrasound field and dots denote differentiation with respect to time. From fundamental fluid dynamic principles, including conversation of mass and momentum, the microbubble scattered pressure $P_{sc}$ due to its vibration can be approximated by

$P_{SC}\approx \rho \frac{\ddot{R}{R}^2+2R{\dot{R}}^2}{r},(2)$

where $r$ is the observational distance from the bubble surface. In the context of ultrasound imaging, bubble activity is commonly separated into two acoustic regimes that give rise to distinct spectral features. Under low amplitude driving conditions at frequency $f$, microbubbles undergo periodic oscillations about their equilibrium size resulting in echoes that possess a rich resonant structure, exhibiting energy at harmonic $\left(nf, n=\mathrm{2,3}\dots \right)$, sub-harmonic $\left(\raisebox{1ex}{$f$}\!\left/ \!\raisebox{-1ex}{$\left(n+1\right)$}\right., n=\mathrm{1,2},\dots \right)$ and ultra-harmonic $\left(\left(2n+1\right)f/2, n=\mathrm{1,2}\dots \right)$ frequency bands (Figures 1A,B). This type of cavitation is called stable (or non-inertial) cavitation, which is typically desired in routine contrast examinations. When the acoustic pressure is increased above a threshold value, microbubbles can rapidly expand and collapse during the compression phase of the ultrasound wave resulting in a transient, high-amplitude echo characterized by broadband emissions. As this bubble collapse is dominated by the inertia of the surrounding fluid, it is often referred to as inertial cavitation [31]. Quantitative indicators of inertial cavitation on an individual microbubble scale have been suggested, including when the maximum bubble radius $R_{max}\ge 2R_0$ otherwise known as the Flynn criteria [32]. The disruption of microbubbles results in an immediate loss of gas and thus in a time-dependent loss of contrast signal. On clinical scanners, the mechanical index $MI=\raisebox{1ex}{$P$}\!\left/ \!\raisebox{-1ex}{$\sqrt{f}$}\right.$, where $P$ is the peak-negative pressure amplitude in MPa and $f$ is the centre frequency in MHz, is a metric used to estimate the likelihood of inertial cavitation and is generally maintained at low values to minimize bubble destruction [33]. Indeed, across the broad spectrum of all clinical contrast imaging applications, it is recommended to start at the manufacturers default contrast MI. If perfusion is still not well visualized after exhausting other image-enhancing strategies (e.g., receiver gain), then the MI should be increased by the smallest increment allowed on the given clinical system [18], with a maximum recommended MI between 0.2–0.3 [34–36]. However, specific techniques have been developed (e.g., disruption-replenishment [37, 38]) whereby short duration, large MI pulses (e.g., high MI flash under the FDA limit of MI = 1.9) are employed to purposefully disrupt microbubbles in the focal volume, followed by a rapid switch back to low MI imaging pulses. The rate at which these bubbles replenish the imaging plane can be used to assess blood flow characteristics upon application of relatively simple models [37, 38]. The specific MI that elicits microbubble disruption has been the subject of much investigation [39–43] and has been shown to be dependent on microbubble formulation, size, and surrounding environment.

FIGURE 1

FIGURE 1. Illustrative microbubble simulations depicting its resonant and nonlinear behaviour. (A) Radius versus time of an oscillating microbubble and (B) it is corresponding frequency content. Note the presence of subharmonic (0.5), ultraharmonic (1.5, 2.5, 3.5) and harmonic (2, 3) energy, as well as energy at the fundamental frequency band (1). (C) The presence of an encapsulating shell serves to increase the resonance frequency and dampen the vibrational amplitude of an otherwise identical microbubble. (D) Under large forcing conditions, microbubbles exhibit asymmetrical resonance, including a shift down in resonance frequency with increasing forcing amplitude. Note here the inherent skewing of the resonance response, typical of a strain-softening resonator.

Ultrasound-driven microbubble response is resonant in nature, and the resonance frequency is one of the important factors in agent design and optimization. Under low acoustic driving conditions, the nonlinear equation of motion Eq. 1 can be reduced to one of a harmonic oscillator with a linear resonance frequency $f_0$ given by:

$f_0=\frac{1}{2\pi }\sqrt{\frac{3\gamma P_0}{\rho R_0^2}+\frac{2\sigma \left(3\gamma -1\right)}{R_0^3}},(3)$

where an inverted relationship between resonance frequency and size can be observed.

The addition of an encapsulating shell has led to adjustments of Eq. 1, which incorporate the viscoelastic properties of the thin shell, i.e., shell stiffness and viscosity. While many models have been developed to capture various aspects of microbubble physics, under low-amplitude transmit pressure conditions they are all in agreement with experimental observations which confirm that the encapsulating layer serves to increase the resonance frequency and the vibration dampening of an otherwise identical bubble (Figure 1C). As driving amplitudes increase, microbubbles display nonlinear resonance phenomena, including strain-softening behavior resulting in asymmetric resonance curves shifting to lower resonance frequencies [44, 45] (see Figure 1D). While these nonlinear behaviors can be generated by unencapsulated gas bubbles [46], the surface rheology of the encapsulation material at megahertz oscillations plays a key role in amplifying these effects [47]. As such, there have been extensive efforts to understand the underlying physics of encapsulated microbubble vibration dynamics, including asymmetric oscillations [48], nonlinear resonance [49], multiple scattering [50], and boundary effects [51].

3 Contrast Pulse Sequences

Nonlinear behavior of vibrating microbubbles is central to their effectiveness as an ultrasound contrast agent. These emissions provide a means to separate bubble signals within small vessels from those of the surrounding (approximately linear) tissue (Figure 2). Original methods of bubble detection consisted of harmonic imaging, whereby energy at the second harmonic (twice the driving frequency) was collected and filtered from the receive signal. Since microbubbles generate much larger second harmonic signal than tissue, this results in better signal-to-noise ratios than that from the fundamental energy. This approach however requires long-duration (narrowband) transmit pulses in order to ensure separation of the spectral components at $f$ and $2f$, as well as to fit within the transducer bandwidth. These conditions result in decreased axial resolution and ultimately a trade-off between image resolution and contrast quality. Multi-pulse contrast imaging pulse sequences, consisting of pulse inversion (PI [23]), amplitude modulation (AM [52]) and combinations thereof (contrast pulse sequences, CPS [53]), have been developed to circumvent these issues to specifically image the blood pool with high specificity and sensitivity. The following sections briefly outline these two main approaches; for a more exhaustive survey of microbubble-specific imaging methods, the reader is referred to a recent review article [54].

FIGURE 2

FIGURE 2. Microbubble-specific imaging sequences capture nonlinear signal from contrast agent while rejecting linear scattering tissue. (A) Schematic diagram depicting the pulse inversion technique. Two pulses that are 180° out of phase will result in tissue echoes that are similarly out of phase. However, this is not the case for microbubbles due to their nonlinear behavior. The summed echo results in near complete cancellation for linear tissue and significant signal from echoes generated from microbubbles. (B) B-mode and (C) contrast-specific imaging of an 8 mm vessel phantom highlights the increased vessel contrast due to microbubble-specific imaging. This was acquired with a Philips iU22 scanner using a C5-2 probe and Definity™ contrast agent.

3.1 Pulse Inversion

The generalized scattered signal from a scatterer $O\left(x\left(t\right)\right)$ can be modeled by a polynomial expansion:

$O\left(x\left(t\right)\right)={\displaystyle \sum _{m=1}^{\infty }{a}_m{x}^m},(4)$

where $x\left(t\right)$ is the transmit waveform. The contributions of the nonlinear components are defined by the coefficients $a_m$, whereby for linear systems only $a_1$ is nonzero. As ultrasound pulses consist of sinusoidal transmit sequences, e.g., $x\left(t\right)=\text{cos}\left(\omega t\right)$ with $\omega =2\pi f$ the angular transmit frequency, the nonlinear echo can be approximated by

$O\left(x\left(t\right)\right)\approx a_1\cos \left(\omega t\right)+\frac{a_2}{2}\left[1+\text{cos}\left(2\omega t\right)\right]+\frac{a_3}{4}\left[\cos \left(\omega t\right)+\cos \left(3\omega t\right)\right]+\frac{a_4}{8}\left[3+4\cos \left(2\omega t\right)+\cos \left(4\omega t\right)\right]+\dots (5)$

Note from the above equation that even-order terms create echoes at even harmonics (and DC), while the odd-order terms account for echoes at the fundamental frequency and odd-order harmonics. The pulse inversion multi-pulse sequence consists of sending in two transmit pulses that are 180° out of phase with each other (Figure 2A). Upon summation of the resulting echoes $s\left(t\right)$, the linear contributions are removed and only even order harmonic signal is retained:

$s\left(t\right)= O_1\left(x\left(t\right)\right)+O_2\left(-x\left(t\right)\right)=2{\displaystyle \sum }_{m=1}^{\infty }{a}_{2m}{x}^{2m}.(6)$

While this technique suppresses fundamental signal, it still requires careful selection of transmit frequency to be able to sensitively detect even order harmonics with the given transducer.

3.2 Amplitude Modulation

In a similar attempt to preserve nonlinear contributions, amplitude modulation consists of transmitting a sequence of pulses that are scaled by a constant factor. Typically, the echoes received from $x_1\left(t\right)$ and $x_2\left(t\right)=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.x_1\left(t\right)$ (referred to as “full amplitude” and “half-amplitude” pulses respectively) are scaled and subtracted, resulting in a residual signal $s\left(t\right)$ defined as:

$s\left(t\right)= O_1\left(x_1\left(t\right)\right)-2O_2\left(\frac{1}{2}{x}_1\left(t\right)\right).(7)$

This results in a signal that partially retains all harmonics, including signal at the fundamental frequency; shown here to third order:

$s\left(t\right)\approx \frac{a_2}{4}\left[1+\cos \left(2\omega t\right)\right]+\frac{3a_3}{16}\left[\cos \left(\omega t\right)+\cos \left(3\omega t\right)\right]+\dots (8)$

It is important to note here that the signal component within Eq. 8 at the driving frequency $\omega$ represents the scaled difference in the fundamental component due to different amounts of nonlinear signal in the two driving pulses. This “nonlinear fundamental” signal results from the fact that microbubbles exhibit nonlinear resonance characteristics, specifically an amplitude dependent resonance frequency (Figure 1D). As such, the fundamental microbubble response will not necessarily be linearly proportional to the input transmit pressure, e.g., the response from $x\left(t\right)$ will not be twice that of $\frac{1}{2}x\left(t\right)$. Indeed, bubble-specific strategies are currently under development that exploit the accompanying echo phase lag associated with this phenomenon [55]. While this approach retains less even-order harmonic energy than PI, the residual “nonlinear fundamental” is particularly useful as it can be well detected within the transducer bandwidth.

Both PI and AM methods can be performed using three or more pulses, offering some advantages in tissue rejection at the cost of temporal resolution. The combination of these two approaches (PIAM, or CPS) retains similar levels of odd-order nonlinear energy as AM while preserving more even-order harmonics, albeit less than the PI technique alone.

4 Emerging Technologies

Contrast-enhanced ultrasound imaging is employed in many clinically approved and off-label applications worldwide. Cutting-edge advancements in this area are being made simultaneously on many fronts, including contrast agent synthesis, the design of novel pulse sequences and image processing techniques, device development, and on the development of remote monitoring for ultrasound therapeutics (Table 2).

TABLE 2

TABLE 2. Summary of emerging ultrasound-microbubble based techniques. See text for references and further details.

4.1 Contrast Agents

Microbubbles are currently the only clinically approved ultrasound contrast agent. One of the strengths of these bubbles is that they remain intravascular due to their size, allowing for diagnostic measurements that would be otherwise difficult with diffusible tracers. However, there is a growing focus to extend the use of these ‘traditional’ ultrasound contrast agents towards other applications, including molecular-based imaging, imaging of the extravascular space, and as a dual imaging and therapeutic delivery platform.

4.1.1 Molecularly Targeted Microbubbles

Non-invasive imaging of pathophysiological events has recently been shown feasible with ultrasound due to the synthesis of functionalized microbubbles [56], i.e., microbubbles with one or more targeting moieties incorporated into the phospholipid encapsulation [57]. Due to the strictly intravascular nature of microbubbles, target sites have aimed at processes that occur within the vasculature, such as inflammation [58], angiogenesis [59], and thrombus formation [60]. This technique has shown significant pre-clinical promise, with agents synthesized to target key endothelial biomarkers involved in disease, e.g., ICAM-1 [61], VCAM-1 [58], ${\alpha }_V{\beta }_3$ [62], E-selectin [63]. Clinical trials to assess safety and tumor detection sensitivity have shown encouraging results using microbubbles functionalized for vascular endothelial growth factor receptor 2 (VEGFR2) in ovarian, breast and prostate cancer [64, 65]. Indeed, this technique can be used as a means for early differential disease detection, as pathological molecular expression often occurs at an earlier timepoint in relation to anatomical changes—but it can also be used as a tool for non-invasive therapy monitoring [66]. In either case, the objective is to establish a proportional relationship between detected bound bubble signal and the level of target molecule expression. Part of this strategy is therefore to preferentially detect signals from bound bubbles, as distinct from freely circulating, or non-bound stationary agent. While there have been some suggestions of novel echo characteristics that would specifically indicate a bound versus unbound bubble [67, 68], imaging techniques to exploit this behavior are not yet used robustly in practice. Instead, a number of approaches have been developed to estimate adherent bubble signal, one of which is to exploit the increased persistence of bound bubbles. Exploiting the relatively short half-life of freely circulating microbubbles, image acquisition ∼10 min post injection will preferentially capture bound bubble signal [69]. Another strategy is to first acquire a baseline image consisting of all bubbles (both bound and unbound) and to apply a large magnitude pulse to disrupt them [56]. Contrast images are then acquired immediately post-disruption to monitor the reperfusion of circulating microbubbles into the imaging plane. The bound-bubble specific image is then estimated as the difference between the pre- and post-burst images. A third approach is to exploit the increased decorrelation due to motion associated with circulating bubbles relative to stationary ones. While this has shown significant promise in pre-clinical testing [70], it is expected to have limitations in regions of substantial tissue motion.

Despite the relative success of the aforementioned bound bubble quantification techniques, only a small fraction the injected microbubbles bind to the activated endothelium, on the order 1–2% [71]. A clever approach to increase the number of microbubbles that make direct contact with the endoluminal border is through the use of acoustic radiation force, originally postulated for such a purpose over two decades ago [72, 73]. Acoustic radiation forces, otherwise known as Bjerknes forces, are the forces imparted to a small object within an acoustic beam by the acoustic wave [7]. In the context of ultrasound-stimulated microbubbles, the primary Bjerknes force magnitude $F$ directed away from the transducer experienced by a resonating microbubble in a pulsed field of duty cycle $D$ and pulse repetition interval $T$ can be estimated as [74].

$F= \frac{P^2{R}_0}{\delta \rho c{f}_0}\left(\frac{D}{T}\right),(9)$

where $\delta$ is the damping coefficient [75] and $c$ is the speed of sound. Secondary Bjerknes force, which is the force ascribed to the translational dynamics between two vibrating microbubbles, can also be shown to be highly dependent on microbubble size and separation distance [74]. While the physical acoustics of these phenomena have long been investigated [76, 77], it has been since utilized as an approach to increase microbubble binding efficiency [74, 78, 79]. Quantification of acoustic radiation force (ARF)-enhanced microbubble imaging can be performed using a relative measure of bubble signal pre- and post-ARF burst, allowing for an attenuation-independent measure of quantification (i.e., one that does not rely on the absolute signal intensity) [80, 81].

4.1.2 Sub-Micron Contrast Agents

Motivated by the enhanced-permeability and retention effect [82], whereby small nanometer sized particles locally extravasate from leaky blood vessels and accumulate in the perivascular space of solid tumors, there are numerous ultrasound-sensitive sub-micron agents currently under investigation. These mainly include phase-shift droplets [83], nanobubbles [84], gas vesicles [85], echogenic liposomes [86], and polymeric nanoparticles [87]. Perhaps the most well-studied of these are volatile, phase-shift sub-micron droplets synthesized from perfluorocarbons (PFCs). As a liquid, droplets provide limited acoustic contrast and are generally not detectable with conventional ultrasound. However, under externally applied ultrasound conditions, these droplets can be acoustically vaporized into detectable, micrometer-sized bubbles approximately 5–10 times their precursor size [91]. Droplet compositions generally consist of PFCs due to their low toxicity, low solubility and their boiling points near physiological temperatures [83], allowing the design of droplets in or near a superheated state. As these superheated droplets are thermodynamically unstable, they are stabilized through phospholipid encapsulation—reducing surface tension and inhibiting diffusion of the PFC into the surrounding medium. Indeed, droplets can be synthesized directly from pre-cursor microbubbles, e.g., commercially employed agents such as Definity™ [88, 89]. While the physics of acoustic droplet vaporization is still an active area of research, the process likely involves both intrinsic (e.g., PFC, encapsulation material) and extrinsic (e.g., sound and its propagation medium) factors. The vaporization threshold of individual droplets empirically exhibits a size-dependence, with larger, micron-sized droplets requiring lower pressures to vaporize [90–92]. Further, there is an increasing threshold with decreasing frequency [93]—indeed these two factors make the vaporization of small, sub-micron droplets at clinically relevant frequencies a challenge. However, recent translational studies using pre-clinical and programmable array systems have shown the feasibility of in-vivo image-guided vaporization and extravascular imaging [94, 95], see Figure 3.

FIGURE 3

FIGURE 3. Estimated droplet extravasation signal is larger in tumor than in kidney. (A) Two successive vaporization sequences (Vaporization 1 and 2) separated by 30 s were transmitted to both the kidney (highly intravascular organ) and tumor xenograft (intravascular and extravascular components) in a mouse model, outlined in the dashed lines. The white arrowheads denote the lack of signal enhancement from the second vaporization pulse within the tumor, suggesting droplet extravasation. Scale bar is 5 mm. (B) Quantification of extravasation signal (p < 0.001). Reprinted by permission of Elsevier from Helfield et al Ultrasound and Medicine and Biology, 2020 [94], see the reference for more details.

As an alternative to phase-shift low-boiling point droplets, recent studies have begun to explore nanobubble contrast agent, typically on the order of several hundred nanometers in size [96]. According to classical models (e.g., Eq. 1 and Eq. 3), nanobubbles are not expected to undergo significant vibrations and scattering at clinically relevant frequencies (e.g., 1–10 MHz). However, studies have demonstrated scattered emissions from nanobubbles at both low [97, 98] and high frequencies [99]. The increased concentration of nanobubbles per unit volume may compensate for the weak scattering from an individual nanobubble, and bubble coalescence (multiple nanobubbles combining to form a microbubble) may also play a role in the observed signal. In addition to these aspects, recent surface modifications (surfactants, e.g., Pluronic) to nanobubble encapsulation layers has been suggested as a potential mechanism to further reduce surface tension and increase flexibility [96, 97]. Regardless of the mechanism, observations of intact nanobubbles in the extravascular space have very recently been documented [100, 101].

Recently, a new and exciting type of biologically-derived, sub-micron ultrasound contrast agent has been developed by harnessing gas vesicles (GVs) [85]. These vesicles, which were originally identified within gas vacuoles of cyanobacteria, function natively to regulate cellular buoyancy for optimal exposure to light and nutrients [102]. GVs are inert, hollow, gas-filled structures formed entirely from protein. The main consistent is a small protein (GVpA) arranged in a linear crystalline array along ribs that form the GV shell and conical caps. A second protein (GVpC) adheres to the outside of the ribs and stabilizes the structure. These vesicles are freely permeable to gases and liquid water is kept out due to surface tension at the hydrophobic inner surface. GVs have been found in many prokaryotes (e.g., bacteria and archaea), and extensive research has concluded that these GVs possess similar morphology and are constructed from a homologous protein. The size and shape of GVs is a function of the species that generate them, but they are typically cylindrical or spindle-liked shaped, with lengths ranging from 0.1 to 2 µm and widths between 45–200 nm [103]. While similar in principle to other pre-formed sub-micron agents, GVs are rigid, non-spherical structures. In the pioneering work by Shapiro et al [85], purified GVs generated from Halobacterium salinarum (Halo) produced robust contrast using a pre-clinical scanner, including nonlinear harmonic content in-vitro and in mouse liver using an amplitude modulation pulse sequence (e.g., Eq. 7). Since then, many experimental and theoretical investigations have confirmed that GVs are able to elicit nonlinear signal and acoustically-mediated collapse in vitro and in-vivo [104, 105], which highlight the potential of GVs to serve as background-subtracted imaging agents. However, perhaps the greatest differentiator between GVs and traditional ultrasound contrast agents is their ability to be genetically modified. Indeed, the acoustic properties of GVs can be modified at the level of their constituent proteins [106], which enables the concept of environmentally-modulated nonlinear contrast signal (e.g., detecting the presence of specific proteases [107]). Further, recent work has demonstrated the capacity of GVs to act as an acoustic reporter gene in mammalian cells (e.g., an acoustic version of an optical reporter like green-fluorescent protein), whereby contrast signal can be correlated to genetic expression [108].

4.2 Super-Harmonic Imaging

As microbubble vibrations possess a rich resonant structure (Figure 1B), there have been recent developments towards generating contrast images using microbubble super-harmonic frequency components, defined as third-order harmonics and higher $\left(nf; n=\mathrm{3,5,6}\dots \right)$. An extension of traditional second harmonic imaging techniques, the selective reception of these higher-frequency signals results in higher image resolution and contrast-to-tissue ratios compared to standard contrast imaging sequences. Due to the bandwidth of standard clinical transducers, which limits its ability to transmit and receive signals at both the fundamental and super-harmonic energy bands, the implementation of this approach requires multiple, independent transducer elements. This can be accomplished by designing novel phased arrays with interleaved elements for transmit and receive [109, 110], and confocally aligned dual-element transducers [111, 112]. Recent incarnations of this approach, termed acoustic angiography [113], performs super-harmonic imaging using transmit frequencies between 2–4 MHz and receives echo signal from 25–30 MHz. Using this device, an in-vivo resolution of 150–200 µm and a contrast-to-tissue ratio of 20 dB has been demonstrated [114, 115]. To date, this technology has been employed to image and assess tumor microcirculation [116, 117] and remains mostly pre-clinical; although very recent work highlights its potential for clinical translation [118, 119] and is currently an active area of research.

4.3 Non-Invasive Pressure Estimation

Local blood pressure estimation provides valuable clinical information on the physiology of many organs, and can be employed in the diagnosis of disease in the heart and kidneys. Most current clinical techniques to assess blood pressure within non-limb vessels use catheter-based manometers, which is an invasive approach and introduces changes to the local blood circulation and thus the blood pressure. Perhaps one of the most impactful applications of non-invasive pressure estimation would be for the early detection of clinically significant portal vein hypertension, defined as an increase in the pressure gradient between the portal vein and hepatic veins exceeding 10 mmHg [120]. As noted almost four decades ago [121], bubble response is a direct function of the ambient hydrostatic pressure and may, in principle, be used as a pressure sensor to detect fluctuations in local blood pressure. An increase in ambient pressure effectively compresses the microbubble, resulting in a shift upwards in resonance frequency. For a given transmit frequency, this will manifest itself in the amplitude of the resulting scattered echo. These original works performed on unshelled bubbles resulted in large uncertainties (as much as 30%, or 50 mmHg compared to reference standards [122]) due to the challenge of detecting the relatively small shift in resonance frequency (∼1 kHz shift from a change in 10 mmHg). While the rheological characteristics of phospholipid encapsulated microbubbles results in much larger resonant shifts (∼0.07–0.24 MHz per 10 mmHg [123]) that may be sufficiently detectable for clinical utility, major advances in this application of remote blood pressure estimation are derived from investigations into the modulation of subharmonic scattering. Based on earlier works on commercially available contrast microbubbles that indicate a decrease in subharmonic scattering with increasing hydrostatic pressure [124], subharmonic-aided pressure estimation efforts (referred to as SHAPE [125]) have met initial success in pre-clinical models [126, 127] and in clinical trials for portal hypertension [128] and intra-cardiac measurements [122].

4.4 Ultrasound Localization Microscopy

A flourishing research area within diagnostic ultrasound is the development, implementation and interpretation of ultrafast ultrasound imaging, in which up to 20 kHz frame rates (compared to 10–100 Hz using conventional scanners) can be achieved through advances in hardware and software. This concept is based off the transmission of an ultrasonic plane wave (i.e., unfocused beam), which avoids the time-consuming process of sequential scanning and beamforming conducted by traditional focused-mode imaging. The echoes from a single plane wave transmission are received by the transducer elements and subsequently processed and beamformed in parallel. While the use of a single, unfocused transmit beam results in poor image resolution, SNR can be markedly increased by transmitting multiple plane waves at different angles and compounding the coherent beamformed images. Despite this slight subsequent reduction in frame rate, this still results in a very fast acquisition relative to conventional focused beam, limited in principle only by the two-way speed of sound in tissue. Ultrafast plane wave imaging has opened an array of contrast and non-contrast ultrasound applications that take advantage of such increased temporal resolution, including ultrafast elastography [129], cardiac [130], and Doppler-based applications [131].

Perhaps the most disruptive technique derived from a microbubble-based application of this technology to date is ultrasound localization microscopy (ULM, see Figure 4) [132]. As a super-resolution imaging technique, it has begun a paradigm shift in biomedical ultrasound imaging applications despite many previous investigations into methods to improve ultrasound imaging resolution. In standard imaging techniques, image resolution is bound by diffraction to the scale of the wavelength; for example, in a 6-MHz ultrasound imaging system ($\lambda =250$ µm), the diffraction limit is 125 µm $\left(\lambda /2\right)$. The ULM approach exploits the localization of microbubbles to finely sample and image the microcirculation beyond the limit imposed by diffraction, showing impressive results in the areas of oncology [116, 133] and neurology [134, 135] that result in an improvement of the resolving power of ultrasound up to a factor of 10 compared to the diffraction limit [136, 137]. It is an approach inspired by the light microscopy counterpart; photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM). These cutting-edge light microscopy techniques, which can image beyond the diffraction limit by an order of magnitude [138–140], rely on photoactivatable fluorescence probes that display unique spectral features upon exposure to different wavelengths of light. These reversible, “photo-switchable” probes in combination with fast-frame imaging cameras enable the rapid acquisition of frames in which only a subset of the sources is visible. With knowledge of the point-spread function of the imaging system, the collection of many sub-wavelength localizations can be reconstructed with resolution lower than the diffraction limit. Indeed, the development of these techniques was so important that it led to the attribution of the 2014 Nobel prize in Chemistry to Eric Betzig, Stefan Hell and William E. Moerner.

FIGURE 4

FIGURE 4. (A) An example of ULM applied in a rat brain through a thinned, intact skull providing a resolution of 10 µm × 8 µm in depth and lateral direction, respectively. (B) In-plane velocity map from parts of the vessel from panel A. Scale bar runs from −14 mm/s (blue) to +14 mm/s (red). Reprinted from [135] with permission from the authors and Nature Publishing Group.

An ultrasonic version of super-resolution is achieved by replacing the fluorescent markers with microbubbles (which are sub-wavelength, individual acoustic sources), and the fast cameras with plane-wave, programmable ultrasound imaging systems. These programmable systems give access to the pre-beamformed time-domain data (RF data), whereby assuming a single source, the signal time delay $\tau$ as a function of array position $x$ produced by a single microbubble echo propagating at a constant speed $c$ is given by:

$\tau =\frac{\sqrt{z_0^2+{\left(x-x_0\right)}^2}}{c} ,(10)$

where $z_0$ and $x_0$ are the depth and lateral position of the microbubble, respectively. One approach to microbubble localization is to fit this delay function (i.e., a parabolic function), the peak of which will provide the position of the microbubble at much higher resolution than the wavelength [132]. Alternatively, even on beamformed images acquired from conventional ultrasound scanners, various algorithms have been developed to estimate the intensity-weighted centroid of an individual microbubble and has shown success in dilute microbubble applications [141, 142].

The general concept of acquiring a super-resolution imaging using ULM will next be outlined here. After injection of a dilute suspension of contrast agent, video acquisition of the location of interest, either using B-mode or contrast-specific sequences, can be taken using either conventional beam or fast-frame plane wave techniques. Since the resulting ULM image is constructed point by point, a sufficient quantity of microbubbles is required to reconstruct the vasculature, on the order of 1 million events [135] depending on the vessel density and field of view. Given the relatively slow blood velocities in the microvasculature, this often requires long image acquisition times and results in a vast amount of data for processing. Motion correction algorithms are next applied to minimize motion-related localization artefacts, which present a particular challenge due to these long scan times. Various techniques have been demonstrated within the context of the ULM workflow, including phase-correlation approaches between successive B-mode images, all of which result in corrections on the order of hundreds of micrometers for in-plane motion [143–145]. While out-of-plane motion correction is not possible using this 2D approach, 3D ULM techniques are currently being assessed [146]. Following this, a microbubble-filtering processing step is introduced, which can include isolating nonlinear emissions [134, 141] as well as alternative image processing strategies including spatiotemporal-based filtering algorithms [135, 145, 147]. Microbubble localization is then performed by estimation of its centroid using either the raw RF data or the beamformed image. A critical challenge here is the reliable separation of one microbubble from another. The most direct way of localizing a single microbubble is to use a low concentration of contrast agent (e.g., ${10}^6$ bubbles/ml) [134, 141, 148], which guarantees an inter-bubble spacing (e.g., 100 $\mu$m) of several imaging wavelengths at traditional transmit frequencies. Even in such instances, the robust SNR generated from an individual microbubble is of paramount importance, and will ultimately affect the ULM resolution. Recent work [149] has suggested that exploiting the phase response of vibrating microbubbles, a property linked to their resonant nature [75], can increase ULM image quality. However, there are emerging alternative strategies that allow for higher local doses of microbubbles, attempting to circumvent the spatial resolution versus acquisition time trade-off inherent to ULM. Increased local microbubble concentrations not only shorten the scan time, but increase the SNR. In order to overcome the overlapping of the point-spread functions, spatiotemporal filtering algorithms to separate overlapping microbubble signals [150, 151] have been introduced. Recently, algorithms based on deep learning (Deep-ULM) have been proposed, offering the advantage of acquiring high resolution images with high microbubble concentrations and lower computation load compared to other techniques. This AI-based approach is capable of learning the nonlinear image domain implications of overlapping point-spread functions originating from populations of closely spaced microbubbles [152]. Finally, tracking of microbubble trajectories, using simple or more complex algorithms [145, 153], allows not only for the estimation of super-resolved blood flow velocities [135, 144], but for improved image quality due to the fact that a single microbubble can reconstruct several pixels during its trajectory. Indeed, as adequate sampling of microbubble location is critical for the success of tracking algorithms, ultrafast imaging techniques offer a major advantage over conventional imaging approaches. Images are often then reconstructed by projecting the detected tracks on a sub-wavelength grid matrix. True estimates of vessel diameter, therefore, cannot rely on sparse tracks but require them in sufficient number to ensure mapping of the entire lumen, a track density determined by the width of the vessel divided by the super-resolved pixel size [154].

While still in its infancy, ULM has already provided a new in-vivo approach to the study of tissue pathology, providing quantitative information on the density, tortuosity, and small modulations of flow patterns within the microvasculature at depth. The first clinical applications of this technology, using conventional focused beam acquisition, have been conducted on breast cancer [155], lower limb assessment [156] and liver imaging [157]. While there are still limitations to this approach, including slow scan times, SNR, the use of plane-wave scanners not typical in clinics, large amounts of data storage and processing, and motion artefacts, significant advancements in all of these areas are currently ongoing.

4.5 Microbubble-Therapy Monitoring

It has long been recognized that ultrasound interactions with biological tissue induce bio-effects of both thermal and mechanical origin [158]. On clinical diagnostic scanners, exposure levels are limited in order to avoid these effects [159]. From a therapeutic standpoint, ultrasound-mediated bioeffects have been investigated as a desired endpoint: with effects ranging from tissue ablation [160], microvascular permeability [161], immunomodulation [162], and vascular occlusion [163]. Recent works have highlighted that microbubble contrast agents, under specific acoustic conditions, can generate a wide spectrum of bioeffects [164–166] that contribute towards the treatment of many diseases. Due to their intravascular nature, a primary avenue of research in microbubble-mediated bioeffects is based on the spatially targeted and temporary enhancement of microvascular permeability, employed to promote local drug delivery to regions of disease. One such promising application is the local and transient opening of the blood-brain-barrier [167, 168] and blood-spinal cord barrier [169, 170] for targeted therapeutics into the central nervous system. This technology has recently entered clinical trials in patients with brain tumors [171–173], Alzheimer’s disease [174] and amyotrophic lateral sclerosis (ALS) [175].

Despite being met with initial success, widespread clinical adoption of microbubble-based therapeutics will require the continued development of online, real-time imaging strategies to guide and control treatments. While some of these applications employ MRI guidance, there is increasing interest in employing the acoustic scattering from the microbubbles themselves as an indicator of treatment outcome. Since the spectral echo characteristics can be indicative of the underlying microbubble vibrations [176], remote detection of these signals during treatment is under investigation as a robust and sensitive tool for therapy guidance. Many preclinical applications of targeted microbubble therapeutics, including cardiovascular disease [177, 178] and cancer [166], are performed as a dual imaging and therapeutic technique. Contrast enhanced ultrasound is applied and interleaved with a therapeutic pulse from either a separate ultrasound transducer [166] or incorporated by way of clinical [179] or custom-designed sequence. In this way, the presence of microbubbles within the anatomical site of interest can be visually confirmed before, during and after the treatment sequences. The acoustic emissions detected during microbubble-based therapies have been identified as potential markers for treatment outcome in applications including blood-brain barrier disruption [180, 181], and targeted therapeutic delivery [182]. To this end, passive cavitation detectors are typically employed to measure raw acoustic data to extract quantitative metrics. Most of these methods to date utilize a single element passive transducer, which does not allow the bubble signal to be localized in space. Ongoing novel engineering of array transducers, combined with passive beamforming algorithms, are currently being designed to spatially map bubble activity and allow for confirmation that elicited bioeffects are localized to the target site [183, 184], see Figure 5. Above and beyond these correlative measures, efforts are underway to establish control feedback algorithms based on the measured bubble acoustic activity to promote safe levels of vibration and avoid more violent, disruptive bubble behaviour that leads to unwanted damage. These algorithms modulate the acoustic transmit parameters based off the real-time feedback from nonlinear microbubble emissions, including sub-harmonic energy [185, 186], harmonic energy [187, 188], or both [189].

FIGURE 5

FIGURE 5. Spatial correlation of ultrafast 3D microbubble cavitation with focused ultrasound (FUS) brain tissue damage in a rabbit model. Baseline $T_{2^{\ast }w}$ (A) and $T_{2w}$ MRI (B) images pre-sonication depict target locations for two focused ultrasound treatment conditions (labeled 1 and 2). Axial, coronal and sagittal $T_{2^{\ast }w}$ MRI images immediately post-sonication [(panels C–F) respectively] depict hypointense regions indicative of tissue damage (dotted lines) overlaid by the corresponding spatial microbubble cavitation data (solid lines). The coronal and sagittal slice volumes are indicated in panel B (yellow lines). Scale bar = 5 mm. Figure modified from Jones et al Theranostics, 2020 [183] with permission from the authors.

5 Conclusion

Ultrasound contrast imaging using microbubbles is a safe and reliable technique for many clinical practices, and its application base is expanding. The tremendous success of this imaging technology to date is courtesy of increased clinical awareness of the benefits of ultrasound, and the collaborative research endeavors between physicists, chemists, engineers, and clinicians on the investigation of microbubble behavior, signal processing techniques, contrast agent synthesis, and device development. In this review, we summarized the fundamentals of contrast agent microbubble vibration and how it is harnessed for routine contrast-imaging application. Specific pulse sequences are employed to extract bubble-specific acoustic signatures and suppress signal arising from the surrounding tissue to enable preferential imaging of the vasculature. We then presented an overview of emerging techniques and technologies associated with microbubble-based imaging, summarized in Table 2. These developments span new design efforts on acoustically-sensitive agents for disease-specific imaging, to new signal processing techniques to obtain highly resolved vascular images, to new interpretation techniques to extract biologically/physiologically relevant data from microbubble acoustic signatures. With the development of new ultrafast imaging technology and image processing techniques, along with increasing interest in targeted ultrasound therapeutic applications, there are still numerous emerging and exciting applications that remain to be explored.

Author Contributions

HY and BH co-led the scientific discussion and writing for this paper. BH is the corresponding author.

Funding

This work is supported, in part, by the NSERC (RGPIN-2019-06969), the Fonds de recherche du Quebec—Nature et Technologies (2021-NC-282699), the Heart and Stroke Foundation of Canada (G-18-0022133), the Canada Research Chair program, and the Burroughs Wellcome Fund (1018212.03). BH holds a Career at the Scientific Interface award from the Burroughs Wellcome Fund (BWF-CASI).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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