Detection and Tracking of Multiple Microbubbles in Ultrasound B-Mode Images

Short Acquisition Time Super-Resolution Ultrasound Microvessel Imaging via Microbubble Separation

Super-resolution ultrasound localization microscopy (ULM), based on localization and tracking of individual microbubbles (MBs), offers unprecedented microvascular imaging resolution at clinically relevant penetration depths. However, ULM is currently limited by the requirement of dilute MB concentrations to ensure spatially sparse MB events for accurate localization and tracking. The corresponding long imaging acquisition times (tens of seconds or several minutes) to accumulate sufficient isolated MB events for full reconstruction of microvasculature preclude the clinical translation of the technique. To break this fundamental tradeoff between acquisition time and MB concentration, in this paper we propose to separate spatially overlapping MB events into sub-populations, each with sparser MB concentration, based on spatiotemporal differences in the flow dynamics (flow speeds and directions). MB localization and tracking are performed for each sub-population separately, permitting more robust ULM imaging of high-concentration MB injections. The superiority of the proposed MB separation technique over conventional ULM processing is demonstrated in flow channel phantom data, and in the chorioallantoic membrane of chicken embryos with optical imaging as an in vivo reference standard. Substantial improvement of ULM is further demonstrated on a chicken embryo tumor xenograft model and a chicken brain, showing both morphological and functional microvasculature details at super-resolution within a short acquisition time (several seconds). The proposed technique allows more robust MB localization and tracking at relatively high MB concentrations, alleviating the need for dilute MB injections, and thereby shortening the acquisition time of ULM imaging and showing great potential for clinical translation.

Super-resolution Ultrasound Imaging

The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

3-D Super-Resolution Ultrasound Imaging With a 2-D Sparse Array

High-frame-rate 3-D ultrasound imaging technology combined with super-resolution processing method can visualize 3-D microvascular structures by overcoming the diffraction-limited resolution in every spatial direction. However, 3-D super-resolution ultrasound imaging using a full 2-D array requires a system with a large number of independent channels, the design of which might be impractical due to the high cost, complexity, and volume of data produced. In this study, a 2-D sparse array was designed and fabricated with 512 elements chosen from a density-tapered 2-D spiral layout. High-frame-rate volumetric imaging was performed using two synchronized ULA-OP 256 research scanners. Volumetric images were constructed by coherently compounding nine-angle plane waves acquired at a pulse repetition frequency of 4500 Hz. Localization-based 3-D super-resolution images of two touching subwavelength tubes were generated from 6000 volumes acquired in 12 s. Finally, this work demonstrates the feasibility of 3-D super-resolution imaging and super-resolved velocity mapping using a customized 2-D sparse array transducer.

Liver contrast-enhanced sonography: computer-assisted differentiation between focal nodular hyperplasia and inflammatory hepatocellular adenoma by reference to microbubble transport patterns

OBJECTIVE

A new computer tool is proposed to distinguish between focal nodular hyperplasia (FNH) and an inflammatory hepatocellular adenoma (I-HCA) using contrast-enhanced ultrasound (CEUS). The new method was compared with the usual qualitative analysis.

METHODS

The proposed tool embeds an "optical flow" algorithm, designed to mimic the human visual perception of object transport in image series, to quantitatively analyse apparent microbubble transport parameters visible on CEUS. Qualitative (visual) and quantitative (computer-assisted) CEUS data were compared in a cohort of adult patients with either FNH or I-HCA based on pathological and radiological results. For quantitative analysis, several computer-assisted classification models were tested and subjected to cross-validation. The accuracies, area under the receiver-operating characteristic curve (AUROC), sensitivity and specificity, positive predictive values (PPVs), negative predictive values (NPVs), false predictive rate (FPRs) and false negative rate (FNRs) were recorded.

RESULTS

Forty-six patients with FNH (n = 29) or I-HCA (n = 17) with 47 tumours (one patient with 2 I-HCA) were analysed. The qualitative diagnostic parameters were accuracy = 93.6%, AUROC = 0.94, sensitivity = 94.4%, specificity = 93.1%, PPV = 89.5%, NPV = 96.4%, FPR = 6.9% and FNR = 5.6%. The quantitative diagnostic parameters were accuracy = 95.9%, AUROC = 0.97, sensitivity = 93.4%, specificity = 97.6%, PPV = 95.3%, NPV = 96.7%, FPR = 2.4% and FNR = 6.6%.

CONCLUSIONS

Microbubble transport patterns evident on CEUS are valuable diagnostic indicators. Machine-learning algorithms analysing such data facilitate the diagnosis of FNH and I-HCA tumours.

KEY POINTS

• Distinguishing between focal nodular hyperplasia and an inflammatory hepatocellular adenoma using dynamic contrast-enhanced ultrasound is sometimes difficult. • Microbubble transport patterns evident on contrast-enhanced sonography are valuable diagnostic indicators. • Machine-learning algorithms analysing microbubble transport patterns facilitate the diagnosis of FNH and I-HCA.

Super-Resolution Imaging Through the Human Skull

High-resolution transcranial ultrasound imaging in humans has been a persistent challenge for ultrasound due to the imaging degradation effects from aberration and reverberation. These mechanisms depend strongly on skull morphology and have high variability across individuals. Here, we demonstrate the feasibility of human transcranial super-resolution imaging using a geometrical focusing approach to efficiently concentrate energy at the region of interest, and a phase correction focusing approach that takes the skull morphology into account. It is shown that using the proposed focused super-resolution method, we can image a 208- [Formula: see text] microtube behind a human skull phantom in both an out-of-plane and an in-plane configuration. Individual phase correction profiles for the temporal region of the human skull were calculated and subsequently applied to transmit-receive a custom focused super-resolution imaging sequence through a human skull phantom, targeting the 208- [Formula: see text] diameter microtube at 68.5 mm in depth and at 2.5 MHz. Microbubble contrast agents were diluted to a concentration of 1.6×10⁶ bubbles/mL and perfused through the microtube. It is shown that by correcting for the skull aberration, the RF signal amplitude from the tube improved by a factor of 1.6 in the out-of-plane focused emission case. The lateral registration error of the tube's position, which in the uncorrected case was 990 [Formula: see text], was reduced to as low as 50 [Formula: see text] in the corrected case as measured in the B-mode images. Sensitivity in microbubble detection for the phase-corrected case increased by a factor of 1.48 in the out-of-plane imaging case, while, in the in-plane target case, it improved by a factor of 1.31 while achieving an axial registration correction from an initial 1885- [Formula: see text] error for the uncorrected emission, to a 284- [Formula: see text] error for the corrected counterpart. These findings suggest that super-resolution imaging may be used far more generally as a clinical imaging modality in the brain.

Ultrasound Imaging

Ultrasound imaging plays an important role in oncological imaging for more than five decades now. It can be applied in all tissues that are not occluded by bone or gas-filled regions. The quality of ultrasound images benefitted strongly from improved electronics and increased computational power. To the morphological imaging, several functional imaging methods were added: Flow visualization became possible by Doppler techniques and as a recent addition the elastic properties of tissues can be imaged by elastographic methods with transient shear wave imaging. In the beginning of molecular imaging, ultrasound with its contrast based on mechanical tissue properties was an unlikely candidate to play a role. However, with contrast agents consisting of micrometer-sized gas bubbles, which can be imaged with high sensitivity, ligands addressing targets in the vascular wall could be used. Because even single bubbles can be detected, this led to various ultrasound molecular imaging techniques and the ongoing development of clinical molecular contrast media. In this chapter, the basic properties of ultrasonic imaging like its contrast mechanisms and spatiotemporal resolution are discussed. The image formation and its ongoing change from line-oriented scanning to full-volume reconstructions are explained. Then, the ultrasound contrast media and imaging techniques are introduced and emerging new methods like super-resolution vascular imaging demonstrate the ongoing development in this field.

Exploiting Flow Dynamics for Superresolution in Contrast-Enhanced Ultrasound

Ultrasound (US) localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert microbubbles (MBs) with low concentration within the bloodstream reveals the vasculature with capillary resolution. Despite its high spatial resolution, low MB concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single superresolved image. Such long acquisition times and stringent constraints on MB concentration are undesirable in many clinical scenarios. To address these restrictions, sparsity-based approaches have recently been developed. These methods reduce the total acquisition time dramatically, while maintaining good spatial resolution in settings with considerable MB overlap. Here, we further improve the spatial resolution and visual vascular reconstruction quality of sparsity-based superresolution US imaging from low-frame rate acquisitions, by exploiting the inherent flow of MBs and utilize their motion kinematics. We also provide quantitative measurements of MB velocities and show that our approach achieves higher MB recall rate than the state-of-the-art techniques, while increasing contrast agents concentration. Our method relies on simultaneous tracking and sparsity-based detection of individual MBs in a frame-by-frame manner, and as such, may be suitable for real-time implementation. The effectiveness of the proposed approach is demonstrated on both simulations and an in vivo contrast-enhanced human prostate scan, acquired with a clinically approved scanner operating at a 10-Hz frame rate.

Super-Resolution Ultrasound Imaging of Skeletal Muscle Microvascular Dysfunction in an Animal Model of Type 2 Diabetes

OBJECTIVES

To evaluate the use of super-resolution ultrasound (SR-US) imaging for quantifying microvascular changes in skeletal muscle using a mouse model of type 2 diabetes.

METHODS

Study groups were young, standard chow-fed male C57BL/6J mice (lean group) and high fat diet-fed older mice (obese group). After an overnight fast, dynamic contrast-enhanced US imaging was performed on the proximal hind limb adductor muscle group for 10 minutes at baseline and again at 1 and 2 hours during administration of a hyperinsulinemic-euglycemic clamp. Dynamic contrast-enhanced US images were collected on a clinical US scanner (Acuson Sequoia 512; Siemens Healthcare, Mountain View, CA) equipped with a 15L8 linear array transducer. Dynamic contrast-enhanced US images were processed with a spatiotemporal filter to remove tissue clutter. Individual microbubbles were localized and counted to create an SR-US image. A frame-by-frame analysis of the microbubble count was generated (ie, time-microbubble count curve [TMC]) to estimate tissue perfusion and microvascular blood flow. The conventional time-intensity curve (TIC) was also generated for comparison.

RESULTS

In vivo SR-US imaging could delineate microvascular structures in the mouse hind limb. Compared with lean animals, insulin-induced microvascular recruitment was attenuated in the obese group. The SR-US-based TMC analysis revealed differences between lean and obese animal data for select microvascular parameters (P < .04), which was not true for TIC-based measurements. Whereas the TMC and TIC microvascular parameters yielded similar temporal trends, there was less variance associated with the TMC-derived values.

CONCLUSIONS

Super-resolution US imaging is a new modality for measuring the microvascular properties of skeletal muscle and dysfunction from type 2 diabetes.

Super-Resolution Contrast-Enhanced Ultrasound Methodology for the Identification of In Vivo Vascular Dynamics in 2D

OBJECTIVES

The aim of this study was to provide an ultrasound-based super-resolution methodology that can be implemented using clinical 2-dimensional ultrasound equipment and standard contrast-enhanced ultrasound modes. In addition, the aim is to achieve this for true-to-life patient imaging conditions, including realistic examination times of a few minutes and adequate image penetration depths that can be used to scan entire organs without sacrificing current super-resolution ultrasound imaging performance.

METHODS

Standard contrast-enhanced ultrasound was used along with bolus or infusion injections of SonoVue (Bracco, Geneva, Switzerland) microbubble (MB) suspensions. An image analysis methodology, translated from light microscopy algorithms, was developed for use with ultrasound contrast imaging video data. New features that are tailored for ultrasound contrast image data were developed for MB detection and segmentation, so that the algorithm can deal with single and overlapping MBs. The method was tested initially on synthetic data, then with a simple microvessel phantom, and then with in vivo ultrasound contrast video loops from sheep ovaries. Tracks detailing the vascular structure and corresponding velocity map of the sheep ovary were reconstructed. Images acquired from light microscopy, optical projection tomography, and optical coherence tomography were compared with the vasculature network that was revealed in the ultrasound contrast data. The final method was applied to clinical prostate data as a proof of principle.

RESULTS

Features of the ovary identified in optical modalities mentioned previously were also identified in the ultrasound super-resolution density maps. Follicular areas, follicle wall, vessel diameter, and tissue dimensions were very similar. An approximately 8.5-fold resolution gain was demonstrated in vessel width, as vessels of width down to 60 μm were detected and verified (λ = 514 μm). Best agreement was found between ultrasound measurements and optical coherence tomography with 10% difference in the measured vessel widths, whereas ex vivo microscopy measurements were significantly lower by 43% on average. The results were mostly achieved using video loops of under 2-minute duration that included respiratory motion. A feasibility study on a human prostate showed good agreement between density and velocity ultrasound maps with the histological evaluation of the location of a tumor.

CONCLUSIONS

The feasibility of a 2-dimensional contrast-enhanced ultrasound-based super-resolution method was demonstrated using in vitro, synthetic and in vivo animal data. The method reduces the examination times to a few minutes using state-of-the-art ultrasound equipment and can provide super-resolution maps for an entire prostate with similar resolution to that achieved in other studies.

3D Super-Resolution US Imaging of Rabbit Lymph Node Vasculature in Vivo by Using Microbubbles

Background Variations in lymph node (LN) microcirculation can be indicative of metastasis. The identification and quantification of metastatic LNs remains essential for prognosis and treatment planning, but a reliable noninvasive imaging technique is lacking. Three-dimensional super-resolution (SR) US has shown potential to noninvasively visualize microvascular networks in vivo. Purpose To study the feasibility of three-dimensional SR US imaging of rabbit LN microvascular structure and blood flow by using microbubbles. Materials and Methods In vivo studies were carried out to image popliteal LNs of two healthy male New Zealand white rabbits aged 6-8 weeks. Three-dimensional, high-frame-rate, contrast material-enhanced US was achieved by mechanically scanning with a linear imaging probe. Individual microbubbles were identified, localized, and tracked to form three-dimensional SR images and super-resolved velocity maps. Acoustic subaperture processing was used to improve image contrast and to generate enhanced power Doppler and color Doppler images. Vessel size and blood flow velocity distributions were evaluated and assessed by using Student paired t test. Results SR images revealed microvessels in the rabbit LN, with branches clearly resolved when separated by 30 µm, which is less than half of the acoustic wavelength and not resolvable by using power or color Doppler. The apparent size distribution of most vessels in the SR images was below 80 µm and agrees with micro-CT data, whereas most of those detected with Doppler techniques were larger than 80 µm in the images. The blood flow velocity distribution indicated that most of the blood flow in rabbit popliteal LN was at velocities lower than 5 mm/sec. Conclusion Three-dimensional super-resolution US imaging using microbubbles allows noninvasive nonionizing visualization and quantification of lymph node microvascular structures and blood flow dynamics with resolution below the wave diffraction limit. This technology has potential for studying the physiologic functions of the lymph system and for clinical detection of lymph node metastasis. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Investigation of Microbubble Detection Methods for Super-Resolution Imaging of Microvasculature

Ultrasound super-resolution techniques use the response of microbubble (MB) contrast agents to visualize the microvasculature. Techniques that localize isolated bubble signals first require detection algorithms to separate the MB and tissue responses. This work explores the three main MB detection techniques for super-resolution of microvasculature. Pulse inversion (PI), differential imaging (DI), and singular value decomposition (SVD) filtering were compared in terms of the localization accuracy, precision, and contrast-to-tissue ratio. MB responses were simulated based on the properties of Sonovue and using the Marmottant model. Nonlinear propagation through tissue was modeled using the k-Wave software package. For the parameters studied, the results show that PI is most appropriate for low frequency applications, but also most dependent on transducer bandwidth. SVD is preferable for high frequency acquisition where localization precision on the order of a few microns is possible. PI is largely independent of flow direction and speed compared to SVD and DI, so is appropriate for visualizing the slowest flows and tortuous vasculature. SVD is unsuitable for stationary MBs and can introduce a localization error on the order of hundreds of microns over the speed range 0-2 mm/s and flow directions from lateral (parallel to probe) to axial (perpendicular to probe). DI is only suitable for flow rates >0.5 mm/s or as flow becomes more axial. Overall, this study develops an MB and tissue nonlinear simulation platform to improve understanding of how different MB detection techniques can impact the super-resolution process and explores some of the factors influencing the suitability of each.

Fast Acoustic Wave Sparsely Activated Localization Microscopy (fast-AWSALM): Ultrasound Super-Resolution using Plane-Wave Activation of Nanodroplets

Localization-based ultrasound super-resolution imaging using microbubble contrast agents and phase-change nano-droplets has been developed to visualize microvascular structures beyond the diffraction limit. However, the long data acquisition time makes the clinical translation more challenging. In this study, fast acoustic wave sparsely activated localization microscopy (fast-AWSALM) was developed to achieve super-resolved frames with sub-second temporal resolution, by using low-boiling-point octafluoropropane nanodroplets and high frame rate plane waves for activation, destruction, as well as imaging. Fast-AWSALM was demonstrated on an in vitro microvascular phantom to super-resolve structures that could not be resolved by conventional B-mode imaging. The effects of the temperature and mechanical index on fast-AWSALM was investigated. Experimental results show that sub-wavelength micro-structures as small as 190 lm were resolvable in 200 ms with plane-wave transmission at a center frequency of 3.5 MHz and a pulse repetition frequency of 5000 Hz. This is about a 3.5 fold reduction in point spread function full-width-half-maximum compared to that measured in conventional B-mode, and two orders of magnitude faster than the recently reported AWSALM under a non-flow/very slow flow situations and other localization based methods. Just as in AWSALM, fast-AWSALM does not require flow, as is required by current microbubble based ultrasound super resolution techniques. In conclusion, this study shows the promise of fast-AWSALM, a super-resolution ultrasound technique using nanodroplets, which can generate super-resolution images in milli-seconds and does not require flow.

Clinical Pilot Application of Super-Resolution US Imaging in Breast Cancer

Recently, we proved in the first measurements of breast carcinomas the feasibility of super-resolution ultrasound (US) imaging by motion-model ultrasound localization microscopy in a clinical setup. Nevertheless, pronounced in-plane and out-of-plane motions, a nonoptimized microbubble injection scheme, the lower frame rate and the larger slice thickness made the processing more complex than in preclinical investigations. Here, we compare the results of state-of-the-art contrast-enhanced to super-resolution US imaging and systematically analyze the measurements to get indications for the improvement of image acquisition and processing in the future clinical studies. In this regard, the application of a saturation model to the reconstructed vessels is shown to be a valuable tool not only to estimate the measurement times necessary to adequately reconstruct the microvasculature but also for the validation of the measurements. The parameters from this model can also serve to optimize contrast agent concentration and injection protocols. Finally, for the measurements of well-perfused tumors, we observed between 28% and 50% filling for 90-s examination times.

3-D Motion Correction for Volumetric Super-Resolution Ultrasound Imaging

Motion during image acquisition can cause image degradation in all medical imaging modalities. This is particularly relevant in 2-D ultrasound imaging, since out-of-plane motion can only be compensated for movements smaller than elevational beamwidth of the transducer. Localization based super-resolution imaging creates even a more challenging motion correction task due to the requirement of a high number of acquisitions to form a single super-resolved frame. In this study, an extension of two-stage motion correction method is proposed for 3-D motion correction. Motion estimation was performed on high volumetric rate ultrasound acquisitions with a handheld probe. The capability of the proposed method was demonstrated with a 3-D microvascular flow simulation to compensate for handheld probe motion. Results showed that two-stage motion correction method reduced the average localization error from 136 to 18 μm.

Resolving Ultrasound Contrast Microbubbles Using Minimum Variance Beamforming

Minimum Variance (MV) beamforming is known to improve the lateral resolution of ultrasound images and enhance the separation of isolated point scatterers. This paper aims to evaluate the adaptive beamformer's performance with flowing microbubbles (MBs) which are relevant to super-resolution ultrasound imaging. Simulations using point scatterer data from single emissions were complemented by an experimental investigation performed using a capillary tube phantom and the Synthetic Aperture Real-time Ultrasound System (SARUS). The MV performance was assessed by the minimum distance that allows the display of two scatterers positioned side-by-side, the lateral Full-Width-at-Half-Maximum (FWHM), and the Peak-Sidelobe-Level (PSL). In the tube, scatterer responses separated by down to [Formula: see text] (or 1.05λ ) were distinguished by the MV method, while the standard Delay-And-Sum (DAS) beamformers were unable to achieve such separation. Up to ninefold FWHM decrease was also measured in favor of the MV beamformer for individual echoes from MBs. The lateral distance between two scatterers impacted on their FWHM value, and additional differences in the scatterers' axial or out-of-plane position also impacted on their size and appearance. The simulation and experimental results were in agreement in terms of lateral resolution. The point scatterer study showed that the proposed MV imaging scheme provided clear resolution benefits compared to DAS. Current super-resolution methods mainly depend on DAS beamformers. Instead, the use of the MV method may provide a larger number of detected, and potentially better localized, MB scatterers.

SUSHI: Sparsity-Based Ultrasound Super-Resolution Hemodynamic Imaging

Identifying and visualizing vasculature within organs and tumors has major implications in managing cardiovascular diseases and cancer. Contrast-enhanced ultrasound scans detect slow-flowing blood, facilitating noninvasive perfusion measurements. However, their limited spatial resolution prevents the depiction of microvascular structures. Recently, super-localization ultrasonography techniques have surpassed this limit. However, they require long acquisition times of several minutes, preventing the detection of hemodynamic changes. We present a fast super-resolution method that exploits sparsity in the underlying vasculature and statistical independence within the measured signals. Similar to super-localization techniques, this approach improves the spatial resolution by up to an order of magnitude compared to standard scans. Unlike super-localization methods, it requires acquisition times of only tens of milliseconds. We demonstrate a temporal resolution of ~25 Hz, which may enable functional super-resolution imaging deep within the tissue, surpassing the temporal resolution limitations of current super-resolution methods, e.g., in neural imaging. The subsecond acquisitions make our approach robust to motion artifacts, simplifying in vivo use of super-resolution ultrasound.

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

Ultrasound super-resolution (SR) microvessel imaging technologies are rapidly emerging and evolving. The unprecedented combination of imaging resolution and penetration promises a wide range of preclinical and clinical applications. This paper concerns spatial quantization error in SR imaging, a common issue that involves a majority of current SR imaging methods. While quantization error can be alleviated by the microbubble localization process (e.g., via upsampling or parametric fitting), it is unclear to what extent the localization process can suppress the spatial quantization error induced by discrete sampling. It is also unclear when low spatial sampling frequency will result in irreversible quantization errors that cannot be suppressed by the localization process. This paper had two goals: 1) to systematically investigate the effect of quantization in SR imaging and establish principles of adequate SR imaging spatial sampling that yield minimal quantization error with proper localization methods and 2) to compare the performance of various localization methods and study the level of tolerance of each method to quantization. We conducted experiments on a small wire target and on a microbubble flow phantom. We found that the Fourier analysis of an oversampled spatial profile of the microbubble signal could provide reliable guidance for selecting beamforming spatial sampling frequency. Among various localization methods, parametric Gaussian fitting and centroid-based localization on upsampled data had better microbubble localization performance and were less susceptible to quantization error than peak intensity-based localization methods. When spatial sampling resolution was low, parametric Gaussian fitting-based localization had the best performance in suppressing quantization error, and could produce acceptable SR microvessel imaging with no significant quantization artifacts. The findings from this paper can be used in practice to help intelligently determine the minimum requirement of spatial sampling for robust microbubble localization to avoid adding or even reduce the burden of computational cost and data storage that are commonly associated with SR imaging.

Microbubble Localization for Three-Dimensional Superresolution Ultrasound Imaging Using Curve Fitting and Deconvolution Methods

Superresolution algorithms in ultrasound imaging are attracting the interest of researchers recently due to the ability of these methods to enable enhanced vascular imaging. In this study, two superresolution imaging methods are compared for postprocessing images of microbubbles generated using passive acoustic mapping (PAM) methods with a potential application of three-dimensional (3-D) brain vascular imaging. The first method is based on fitting single bubble images one at a time with a 3-D Gaussian profile to localize the microbubbles and a superresolution image is then formed using the uncertainty of the localization as the standard deviation of the Gaussian profile. The second superresolution method is based on image deconvolution that processes multiframe resolution-limited images iteratively and estimates the intensity at each pixel of the superresolution image without the need for localizing each microbubble. The point spread function is approximated by a Gaussian curve which is similar to the beam response of the hemispherical transducer array used in our experimental setup. The Cramér-Rao Bounds of the two estimation techniques are derived analytically and the performance of these techniques is compared through numerical simulations based on experimental PAM images. For linear and sinusoidal traces, the localization errors between the estimated peaks by the fitting-based method and the actual source locations were 220 10 m and 210 5 m, respectively, as compared to 74 10 m and 59 8 m with the deconvolution-based method. However, in terms of the running time and the computational costs, the curve fitting technique outperforms the deconvolution-based approach.

Adaptive Multifocus Beamforming for Contrast-Enhanced-Super-Resolution Ultrasound Imaging in Deep Tissue

Contrast-enhanced-super-resolution ultrasound imaging, also referred to as ultrasound localization microscopy, can resolve vessels that are smaller than the diffraction limit and has recently been able to generate super-resolved vascular images of shallow in vivo structures in small animals. To fully translate this technology to the clinic, it is advantageous to be able to detect microbubbles at deeper locations in tissue while maintaining a short acquisition time. Current implementations of this imaging method rely on plane-wave imaging. This method has the advantage of maximizing the frame rate, which is important due to the large amount of frames required for super-resolution processing. However, the wide planar beam used to illuminate the field of view produces poor contrast and low sensitivity bubble detection. Here, we propose an "adaptive multifocus" sequence, a new ultrasound imaging sequence that combines the high frame rate feature of a plane wave with the increased bubble detection sensitivity of a focused beam. This sequence simultaneously sonicates two or more foci with a single emission, hence retaining a high frame rate, yet achieving improved sensitivity to microbubbles. In the limit of one target, the beam reduces to a conventional focused transmission; and for an infinite number of targets, it converges to plane-wave imaging. Numerical simulations, using the full-wave code, are performed to compare the point spread function of the proposed sequence to that generated by the plane-wave emission. Our numerical results predict an improvement of up to 15 dB in the signal-to-noise ratio. Ex vivo experiments of a tissue-embedded microtube phantom are used to generate super-resolved images and to compare the adaptive beamforming approach to plane-wave imaging. These experimental results show that the adaptive multifocus sequence successfully detects 744 microbubble events at 60 mm when they are undetectable by the plane-wave sequence under the same imaging conditions. At a shallower depth of 44 mm, the proposed adaptive multifocus method detects 6.9 times more bubbles than plane-wave imaging (1763 versus 257 bubble events).

Semi-Automated Segmentation of the Tumor Vasculature in Contrast-Enhanced Ultrasound Data

The vascular architecture in tumors contains relevant information for tumor classification and evaluation of therapy responses. To develop a reliable and user-independent analysis tool, a foreground detection algorithm was combined with a maximum-intensity projection to obtain a high signal-to-noise image from contrast-enhanced B-mode data sets, enabling vessel segmentation by thresholding. Parameters describing the density of the vascular network, the number of vessels and the number of branches were extracted. The highly angiogenic A431 tumors had a relative blood volume of 49%, a mean pixel distance to the next vessel of 1.8 ± 0.3 px, 51 ± 29 individual vessels and 478 ± 184 branching points, whereas the more mature and heterogeneous vascularized human epithelial ovarian carcinoma (MLS) and A549 tumors had values of 30%, 3.7 ± 2.7 px, 65 ± 12 and 220 ± 159, and 13%, 7.4 ± 2 px, 31 ± 9 and 59 ± 40, respectively. Thus, our semi-automated analysis method enables the extraction of quantitative vascular features that may help to simplify and standardize tumor characterization.

Ultrasound Localization Microscopy and Super-Resolution: A State of the Art

Because it drives the compromise between resolution and penetration, the diffraction limit has long represented an unreachable summit to conquer in ultrasound imaging. Within a few years after the introduction of optical localization microscopy, we proposed its acoustic alter ego that exploits the micrometric localization of microbubble contrast agents to reconstruct the finest vessels in the body in-depth. Various groups now working on the subject are optimizing the localization precision, microbubble separation, acquisition time, tracking, and velocimetry to improve the capacity of ultrasound localization microscopy (ULM) to detect and distinguish vessels much smaller than the wavelength. It has since been used in vivo in the brain, the kidney, and tumors. In the clinic, ULM is bound to improve drastically our vision of the microvasculature, which could revolutionize the diagnosis of cancer, arteriosclerosis, stroke, and diabetes.

A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging

The emergence of super-resolution imaging makes it possible to display the microvasculatures clearly using ultrasound imaging, which is of great importance in the early diagnosis of cancer. At present, the super-resolution performance can only be achieved when the sampling signal is long enough (usually more than 10,000 frames). Thus, the imaging time resolution is not suitable for clinical use. In this paper, we proposed a novel super-resolution reconstruction method, which is proved to have a satisfactory resolution using shorter sampling signal sequences. In the microbubble localization step, the integrated form of the 2D Gaussian function is innovatively adopted for image deconvolution in our method, which enhances the accuracy of microbubble positioning. In the trajectory tracking step, for the first time the averaged shifted histogram technique is presented for the visualization, which greatly improves the precision of reconstruction. In vivo experiments on rabbits were conducted to verify the effectiveness of the proposed method. Compared to the conventional reconstruction method, our method significantly reduces the Full-Width-at-Half-Maximum (FWHM) by 50% using only 400-frame signals. Besides, there is no significant increase in the running time using the proposed method. Considering its imaging performance and used frame number, the conclusion can be drawn that the proposed method advances the application of super-resolution imaging to the clinical use with a much higher time resolution.

Two-Stage Motion Correction for Super-Resolution Ultrasound Imaging in Human Lower Limb

The structure of microvasculature cannot be resolved using conventional ultrasound (US) imaging due to the fundamental diffraction limit at clinical US frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a superresolved image, which usually requires seconds to minutes of acquisition. Over this time interval, motion is inevitable and tissue movement is typically a combination of large- and small-scale tissue translation and deformation. Therefore, super-resolution (SR) imaging is prone to motion artifacts as other imaging modalities based on multiple acquisitions are. This paper investigates the feasibility of a two-stage motion estimation method, which is a combination of affine and nonrigid estimation, for SR US imaging. First, the motion correction accuracy of the proposed method is evaluated using simulations with increasing complexity of motion. A mean absolute error of 12.2 was achieved in simulations for the worst-case scenario. The motion correction algorithm was then applied to a clinical data set to demonstrate its potential to enable in vivo SR US imaging in the presence of patient motion. The size of the identified microvessels from the clinical SR images was measured to assess the feasibility of the two-stage motion correction method, which reduced the width of the motion-blurred microvessels to approximately 1.5-fold.

Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization

Super-resolution imaging methods promote tissue characterization beyond the spatial resolution limits of the devices and bridge the gap between histopathological analysis and non-invasive imaging. Here, we introduce motion model ultrasound localization microscopy (mULM) as an easily applicable and robust new tool to morphologically and functionally characterize fine vascular networks in tumors at super-resolution. In tumor-bearing mice and for the first time in patients, we demonstrate that within less than 1 min scan time mULM can be realized using conventional preclinical and clinical ultrasound devices. In this context, next to highly detailed images of tumor microvascularization and the reliable quantification of relative blood volume and perfusion, mULM provides multiple new functional and morphological parameters that discriminate tumors with different vascular phenotypes. Furthermore, our initial patient data indicate that mULM can be applied in a clinical ultrasound setting opening avenues for the multiparametric characterization of tumors and the assessment of therapy response.

The vascular structure of tumors impacts diagnosis, prognosis and drug response; however, imaging methods to analyse this important feature have been hindered by spatial resolution limitations. Here the authors present a tool called motion model ultrasound localization microscopy to morphologically and functionally characterize fine vascular networks in tumors at super-resolution.

Improved Super-Resolution Ultrasound Microvessel Imaging With Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

Super-resolution ultrasound microvessel imaging with contrast microbubbles has recently been proposed by multiple studies, demonstrating outstanding resolution with high potential for clinical applications. This paper aims at addressing the potential noise issue in in vivo human super-resolution imaging with ultrafast plane-wave imaging. The rich spatiotemporal information provided by ultrafast imaging presents features that allow microbubble signals to be separated from background noise. In addition, the high-frame-rate recording of microbubble data enables the implementation of robust tracking algorithms commonly used in particle tracking velocimetry. In this paper, we applied the nonlocal means (NLM) denoising filter on the spatiotemporal domain of the microbubble data to preserve the microbubble tracks caused by microbubble movement and suppress random background noise. We then implemented a bipartite graph-based pairing method with the use of persistence control to further improve the microbubble signal quality and microbubble tracking fidelity. In an in vivo rabbit kidney perfusion study, the NLM filter showed effective noise rejection and substantially improved microbubble localization. The bipartite graph pairing and persistence control demonstrated further noise reduction, improved microvessel delineation, and a more consistent microvessel blood flow speed measurement. With the proposed methods and freehand scanning on a free-breathing rabbit, a single microvessel cross-sectional profile with full-width at half-maximum of could be imaged at approximately 2-cm depth (ultrasound transmit center frequency = 8 MHz, theoretical spatial resolution ). Cortical microvessels that are apart can also be clearly separated. These results suggest that the proposed methods have good potential in facilitating robust in vivo clinical super-resolution microvessel imaging.

Development of a Fluid Dynamic Model for Quantitative Contrast-Enhanced Ultrasound Imaging

Contrast-enhanced ultrasound (CEUS) is a non-invasive imaging technique extensively used for blood perfusion imaging of various organs. This modality is based on the acoustic detection of gas-filled microbubble contrast agents used as intravascular flow tracers. Recent efforts aim at quantifying parameters related to the enhancement in the vascular compartment using time-intensity curve (TIC), and at using these latter as indicators for several pathological conditions. However, this quantification is mainly hampered by two reasons: first, the quantification intrinsically solely relies on temporal intensity variation, the explicit spatial transport of the contrast agent being left out. Second, the exact relationship between the acquired US-signal and the local microbubble concentration is hardly accessible. This paper introduces the use of a fluid dynamic model for the analysis of dynamic CEUS (DCEUS), in order to circumvent the two above-mentioned limitations. A new kinetic analysis is proposed in order to quantify the velocity amplitude of the bolus arrival. The efficiency of proposed methodology is evaluated both in-vitro, for the quantitative estimation of microbubble flow rates, and in-vivo, for the classification of placental insufficiency (control versus ligature) of pregnant rats from DCEUS. Besides, for the in-vivo experimental setup, we demonstrated that the proposed approach outperforms the performance of existing TIC-based methods.

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Acoustic super-resolution imaging has allowed the visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7-cm depth and 2.3-MHz center frequency. We corroborate these findings with simulation results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental cross-sectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 times reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection, and 2-D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is, therefore, expected to considerably improve the accuracy of super-resolution.

3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

Standard clinical ultrasound (US) imaging frequencies are unable to resolve microvascular structures due to the fundamental diffraction limit of US waves. Recent demonstrations of 2-D super-resolution both in vitro and in vivo have demonstrated that fine vascular structures can be visualized using acoustic single bubble localization. Visualization of more complex and disordered 3-D vasculature, such as that of a tumor, requires an acquisition strategy which can additionally localize bubbles in the elevational plane with high precision in order to generate super-resolution in all three dimensions. Furthermore, a particular challenge lies in the need to provide this level of visualization with minimal acquisition time. In this paper, we develop a fast, coherent US imaging tool for microbubble localization in 3-D using a pair of US transducers positioned at 90°. This allowed detection of point scatterer signals in 3-D with average precisions equal to [Formula: see text] in axial and elevational planes, and [Formula: see text] in the lateral plane, compared to the diffraction limited point spread function full-widths at half-maximum of 488, 1188, and [Formula: see text] of the original imaging system with a single transducer. Visualization and velocity mapping of 3-D in vitro structures was demonstrated far beyond the diffraction limit. The capability to measure the complete flow pattern of blood vessels associated with disease at depth would ultimately enable analysis of in vivo microvascular morphology, blood flow dynamics, and occlusions resulting from disease states.

A Temporal and Spatial Analysis Approach to Automated Segmentation of Microbubble Signals in Contrast-Enhanced Ultrasound Images: Application to Quantification of Active Vascular Density in Human Lower Limbs

Contrast-enhanced ultrasound (CEUS) using microbubble contrast agents has shown great promise in visualising and quantifying active vascular density. Most existing approaches for vascular density quantification using CEUS are calculated based on image intensity and are susceptible to confounding factors and imaging artefact. Poor reproducibility is a key challenge to clinical translation. In this study, a new automated temporal and spatial signal analysis approach is developed for reproducible microbubble segmentation and quantification of contrast enhancement in human lower limbs. The approach is evaluated in vitro on phantoms and in vivo in lower limbs of healthy volunteers before and after physical exercise. In this approach, vascular density is quantified based on the relative areas microbubbles occupy instead of their image intensity. Temporal features of the CEUS image sequences are used to identify pixels that contain microbubble signals. A microbubble track density (MTD) measure, the ratio of the segmented microbubble area to the whole tissue area, is calculated as a surrogate for active capillary density. In vitro results reveal a good correlation (r² = 0.89) between the calculated MTD measure and the known bubble concentration. For in vivo results, a significant increase (129% in average) in the MTD measure is found in lower limbs of healthy volunteers after exercise, with excellent repeatability over a series of days (intra-class correlation coefficient = 0.96). This compares to the existing state-of-the-art approach of destruction and replenishment analysis on the same patients (intra-class correlation coefficient ≤0.78). The proposed new approach shows great potential as an accurate and highly reproducible clinical tool for quantification of active vascular density.