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

Improved Microbubble Tracking for Super-Resolution Ultrasound Localization Microscopy using a Bi-Directional Long Short-term Memory Neural Network

Ultrasound localization microscopy (ULM) enabled high-accuracy measurements of microvessel flow beyond the resolution limit of conventional ultrasound imaging by utilizing contrast microbubbles (MBs) as point targets. Robust tracking of MBs is an essential task for fast and high-quality ULM image reconstruction. Existing MB tracking methods suffer from challenging imaging scenarios such as high-density MB distributions, fast blood flow, and complex flow dynamics. Here we present a deep learning-based MB pairing and tracking method based on a bi-directional long short-term memory neural network for ULM. The proposed method integrates multiparametric MB characteristics to facilitate more robust and accurate MB pairing and tracking. The method was validated on a simulation data set, a tissue-mimicking flow phantom, and in vivo on a mouse and rat brain.

Microbubble tracking based on partial smoothing-based adaptive generalized labelled Multi-Bernoulli filter for super-resolution imaging

Super-resolution ultrasound (SRUS) can image the vasculature at microscopic resolution according to microbubble (MB) localization, with velocity vector maps obtained based on MB tracking information. High MB concentrations can reduce the acquisition time of SRUS imaging, however adjacent and intersecting vessels are difficult to distinguish, thus decreasing resolution. Low acquisition frame rates affect the precision of flow velocity estimation. This study proposes a partial smoothing-based adaptive generalized labeled multi-Bernoulli filter (SAGLMB) to precisely track the MB motion at different flow velocities. SAGLMB employs a generalized labelled multi-Bernoulli filter (GLMB) for MB trajectory allocation to separate adjacent and intersecting vessels. Furthermore, the nonlinear motion of MB was predicted by an unscented Kalman filter, and a cardinalized probability hypothesis density filter was applied to suppress clutter interference. Finally, the trajectories were smoothed by unscented Rauch-Tung-Striebel to improve the resolution of the SRUS image. The simulation results demonstrate that SAGLMB outperforms the conventional bipartite graph-based tracking at high MB concentrations, achieving at least an 8.55 % improvement in the correctly paired precision, with 3 times increase in the structural similarity index measure. Moreover, SAGLMB can obtain more precise flow velocity estimations with a 4 times improvement than the conventional method. The SRUS results of rabbit kidney show that the proposed method significantly improves resolution of adjacent and intersecting vessels at higher MB concentrations and maintains this performance as the acquisition frame rate decreases. Furthermore, the rat brain microvascular network was reconstructed with 9.21 μm (λ/11.1) resolution. Therefore, SAGLMB can achieve robust SRUS imaging at high concentrations and low acquisition frame rates.

Ultrasound-triggered drug release and cytotoxicity of microbubbles with diverse drug attributes

Ultrasound (US)-triggered cavitation of drug-loaded microbubbles (MBs) represents a promising approach for targeted drug delivery, with substantial benefits attainable through precise control over drug release dosage and form. This study investigates Camptothecin-loaded MBs (CPT-MBs) and Doxorubicin-loaded MBs (DOX-MBs), focusing on how properties such as hydrophilicity, hydrophobicity, and charged functional groups affect their interaction with the lipid surfaces of MBs, thereby influencing the fundamental characteristics and acoustic properties of the drug-loaded MBs. In comparison to DOX-MBs, CPT-MBs showed larger MB size (2.2 ± 0.3 and 1.4 ± 0.1 μm, respectively), a 2-fold increase in drug loading, and an 18 % reduction in leakage after 2 h at 37℃. Under 1 MHz US with a 100 ms pulse repetition interval (PRI), 1000 cycles, 5-minute duration, and 550 kPa acoustic pressure, CPT-MBs undergo inertial cavitation, while DOX-MBs undergo stable cavitation. Drug particles released from these MBs under US-induced cavitation were analyzed using dynamic light scattering, NanoSight, cryo-electron microscopy, and density gradient ultracentrifugation. Results showed that CPT-MBs mainly release free CPT, while DOX-MBs release multilayered DOX-lipid aggregates. The cytotoxicity to C6 cells induced by US-triggered cavitation of these two types of MBs also differed. DOX-lipid aggregates delayed initial uptake, leading to less pronounced short-term (2 h) effects compared to the rapid release of free CPT from CPT-MBs. These findings underscore the need to optimize drug delivery strategies by fine-tuning MB composition and US parameters to control drug release kinetics and achieve the best tumoricidal outcomes.

Underestimation of Flow Velocity in 2-D Super-Resolution Ultrasound Imaging

Velocity estimation in ultrasound imaging is a technique to measure the speed and direction of blood flow. The flow velocity in small blood vessels, i.e., arterioles, venules, and capillaries, can be estimated using super-resolution ultrasound imaging (SRUS). However, the vessel width in SRUS is relatively small compared with the full-width-half-maximum of the ultrasound beam in the elevation direction, which directly impacts the velocity estimation. By taking into consideration the small vessel widths in SRUS, it is hypothesized that the velocity is underestimated in 2-D SRUS when the vessel diameter is smaller than the full width at half maximum elevation resolution of the transducer (FWHMy). A theoretical model is introduced to show that the velocity of a 3-D parabolic velocity profile is underestimated by up to 33% in 2-D SRUS, if the width of the vessel is smaller than FWHMy. This model was tested using Field II simulations and 3-D-printed micro-flow hydrogel phantom measurements. A Verasonics Vantage 256 scanner and a GE L8-18i-D linear array transducer with FWHMy of approximately at the elevation focus were used in the simulations and measurements. Simulations of different parabolic velocity profiles showed that the velocity underestimation was 36.8% % (mean ± standard deviation). The measurements showed that the velocity was underestimated by 30% %. Moreover, the results of vessel diameters, ranging from FWHMy to FWHMy, indicate that velocities are estimated according to the theoretical model. The theoretical model can, therefore, be used for the compensation of velocity estimates under these circumstances.

Influence of Image Discretization and Patch Size on Microbubble Localization Precision

For ultrasound localization microscopy, the localization of microbubbles (MBs) is an essential part to obtain super-resolved maps of the vasculature. This article analyzes the impact of image discretization and patch size on the precision of different MB localization methods to reconcile different observations from previous studies, provide an estimate of feasible localization precision, and derive guidelines for an optimal parameter selection. For this purpose, the images of MBs were simulated with Gaussian point-spread functions (PSFs) of varying width parameter at randomly generated subpixel positions, and Rician distributed noise was added. Four localization methods were tested on the patches of different sizes (number of pixels ): Gaussian fit (GF), radial symmetry (RS) method, calculation of center of mass (CoM), and peak detection (PD). Additionally, the Cramér-Rao lower bound (CRLB) for the given estimation problem was calculated. Our results show that the localization precision is strongly influenced by the ratio of the PSF width parameter to the pixel size , as well as the patch size N. The best parameter combination depends on the localization method. Generally, very small ratios as well as large ratios in combination with small N lead to performance degradation. The GF as a representative of a model-based fit comes close to the CRLB and always performs best for the ratios given by image discretization if N is adapted to the PSF. To achieve good results with the GF and the RS method, a good rule of thumb is to set the pixel sizes and the patch sizes .

Ultrasound Localization Microscopy Precision of Clinical 3-D Ultrasound Systems

Ultrasound localization microscopy (ULM) is becoming well established in preclinical applications. For its translation into clinical practice, the localization precision achievable with commercial ultrasound (US) scanners is crucial-especially with volume imaging, which is essential for dealing with out-of-plane motion. Here, we propose an easy-to-perform method to estimate the localization precision of 3-D US scanners. With this method, we evaluated imaging sequences of the Philips Epiq 7 US device using the X5-1 and the XL14-3 matrix transducers and also tested different localization methods. For the X5-1 transducer, the best lateral, elevational, and axial precision was 109, 95, and m for one contrast mode, and 29, 22, and m for the other. The higher frequency XL14-3 transducer yielded precisions of 17, 38, and m using the harmonic imaging mode. Although the center of mass was the most robust localization method also often providing the best precision, the localization method has only a minor influence on the localization precision compared to the impact by the imaging sequence and transducer. The results show that with one of the imaging modes of the X5-1 transducer, precisions comparable to the XL14-3 transducer can be achieved. However, due to localization precisions worse than m, reconstruction of the microvasculature at the capillary level will not be possible. These results show the importance of evaluating the localization precision of imaging sequences from different US transducers or scanners in all directions before using them for in vivo measurements.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network

Ultrasound localization microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical tradeoff between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps such as pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the gated recurrent unit-based multitasking temporal neural network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. In addition, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer replacements to systematically evaluate the performance of various temporal neural networks, including recurrent neural network (RNN), long short-term memory network (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. In addition, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. The model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

Comparison of contrast-enhanced ultrasound imaging (CEUS) and super-resolution ultrasound (SRU) for the quantification of ischaemia flow redistribution: a theoretical study

The study of microcirculation can reveal important information related to pathology. Focusing on alterations that are represented by an obstruction of blood flow in microcirculatory regions may provide an insight into vascular biomarkers. The current in silico study assesses the capability of contrast enhanced ultrasound (CEUS) and super-resolution ultrasound imaging (SRU) flow-quantification to study occlusive actions in a microvascular bed, particularly the ability to characterise known and model induced flow behaviours. The aim is to investigate theoretical limits with the use of CEUS and SRU in order to propose realistic biomarker targets relevant for clinical diagnosis. Results from CEUS flow parameters display limitations congruent with prior investigations. Conventional resolution limits lead to signals dominated by large vessels, making discrimination of microvasculature specific signals difficult. Additionally, some occlusions lead to weakened parametric correlation against flow rate in the remainder of the network. Loss of correlation is dependent on the degree to which flow is redistributed, with comparatively minor redistribution correlating in accordance with ground truth measurements for change in mean transit time,dMTT(CEUS,R = 0.85; GT,R = 0.82) and change in peak intensity,dIp(CEUS,R = 0.87; GT,R = 0.96). Major redistributions, however, result in a loss of correlation, demonstrating that the effectiveness of time-intensity curve parameters is influenced by the site of occlusion. Conversely, results from SRU processing provides accurate depiction of the anatomy and dynamics present in the vascular bed, that extends to individual microvessels. Correspondence between model vessel structure displayed in SRU maps with the ground truth was>91%for cases of minor and major flow redistributions. In conclusion, SRU appears to be a highly promising technology in the quantification of subtle flow phenomena due ischaemia induced vascular flow redistribution.

Super-Resolution Ultrasound Imaging Using the Erythrocytes-Part I: Density Images

A new approach for vascular super-resolution (SR) imaging using the erythrocytes as targets (SUper-Resolution ultrasound imaging of Erythrocytes (SURE) imaging) is described and investigated. SURE imaging does not require fragile contrast agent bubbles, making it possible to use the maximum allowable mechanical index (MI) for ultrasound scanning for an increased penetration depth. A synthetic aperture (SA) ultrasound sequence was employed with 12 virtual sources (VSs) using a 10-MHz GE L8-18i-D linear array hockey stick probe. The axial resolution was [Formula: see text]m) and the lateral resolution was [Formula: see text]m). Field IIpro simulations were conducted on 12.5- μ m radius vessel pairs with varying separations. A vessel pair with a separation of 70 μ m could be resolved, indicating a SURE image resolution below half a wavelength. A Verasonics research scanner was used for the in vivo experiments to scan the kidneys of Sprague-Dawley rats for up to 46 s to visualize their microvasculature by processing from 0.1 up to 45 s of data for SURE imaging and for 46.8 s for SR imaging with a SonoVue contrast agent. Afterward, the renal vasculature was filled with the ex vivo micro-computed tomography (CT) contrast agent Microfil, excised, and scanned in a micro-CT scanner at both a 22.6- μ m voxel size for 11 h and for 20 h in a 5- μ m voxel size for validating the SURE images. Comparing the SURE and micro-CT images revealed that vessels with a diameter of 28 μ m, five times smaller than the ultrasound wavelength, could be detected, and the dense grid of microvessels in the full kidney was shown for scan times between 1 and 10 s. The vessel structure in the cortex was also similar to the SURE and SR images. Fourier ring correlation (FRC) indicated a resolution capability of 29 μ m. SURE images are acquired in seconds rather than minutes without any patient preparation or contrast injection, making the method translatable to clinical use.

Super-Resolution Ultrasound Imaging Using the Erythrocytes-Part II: Velocity Images

Super-resolution ultrasound imaging using the erythrocytes (SURE) has recently been introduced. The method uses erythrocytes as targets instead of fragile microbubbles (MBs). The abundance of erythrocyte scatterers makes it possible to acquire SURE data in just a few seconds compared with several minutes in ultrasound localization microscopy (ULM) using MBs. A high number of scatterers can reduce the acquisition time; however, the tracking of uncorrelated and high-density scatterers is quite challenging. This article hypothesizes that it is possible to detect and track erythrocytes as targets to obtain vascular flow images. A SURE tracking pipeline is used with modules for beamforming, recursive synthetic aperture (SA) imaging, motion estimation, echo canceling, peak detection, and recursive nearest-neighbor (NN) tracker. The SURE tracking pipeline is capable of distinguishing the flow direction and separating tubes of a simulated Field II phantom with 125-25- [Formula: see text] wall-to-wall tube distances, as well as a 3-D printed hydrogel micr-flow phantom with 100-60- [Formula: see text] wall-to-wall channel distances. The comparison of an in vivo SURE scan of a Sprague-Dawley rat kidney with ULM and micro-computed tomography (CT) scans with voxel sizes of 26.5 and [Formula: see text] demonstrated consistent findings. A microvascular structure composed of 16 vessels exhibited similarities across all imaging modalities. The flow direction and velocity profiles in the SURE scan were found to be concordant with those from ULM.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

An Automated Heart Shunt Recognition Pipeline Using Deep Neural Networks

Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice, enabling non-experts to assess heart shunt lesions. This study aims to develop a fully automated and scalable analysis pipeline for distinguishing heart shunts, utilizing a deep neural network-based framework. The pipeline consists of three steps: (1) chamber segmentation, (2) ultrasound microbubble localization, and (3) disease classification model establishment. The study's normal control group included 91 patients with intracardiac shunts, 61 patients with extracardiac shunts, and 84 asymptomatic individuals. Participants' SC-TTE images were segmented using the U-Net model to obtain cardiac chambers. The segmentation results were combined with ultrasound microbubble localization to generate multivariate time series data on microbubble counts in each chamber. A classification model was then trained using this data to distinguish between intracardiac and extracardiac shunts. The proposed framework accurately segmented heart chambers (dice coefficient = 0.92 ± 0.1) and localized microbubbles. The disease classification model achieved high accuracy, sensitivity, specificity, F1 score, kappa value, and AUC value for both intracardiac and extracardiac shunts. For intracardiac shunts, accuracy was 0.875 ± 0.008, sensitivity was 0.891 ± 0.002, specificity was 0.865 ± 0.012, F1 score was 0.836 ± 0.011, kappa value was 0.735 ± 0.017, and AUC value was 0.942 ± 0.014. For extracardiac shunts, accuracy was 0.902 ± 0.007, sensitivity was 0.763 ± 0.014, specificity was 0.966 ± 0.008, F1 score was 0.830 ± 0.012, kappa value was 0.762 ± 0.017, and AUC value was 0.916 ± 0.006. The proposed framework utilizing deep neural networks offers a fast, convenient, and accurate method for identifying intracardiac and extracardiac shunts. It aids in shunt recognition and generates valuable quantitative indices, assisting clinicians in diagnosing these conditions.

Metabolic Pathways in Hydrocephalus: Profiling with Proteomics and Advanced Imaging

Hemorrhagic hydrocephalus is a common pathology in neonates with high mortality and morbidity. Current imaging approaches fail to capture the mechanisms behind its pathogenesis. Here, we discuss the processes underlying this pathology, the metabolic dysfunction that occurs as a result, and the ways in which these metabolic changes inform novel methods of clinical imaging. The imaging advances described allow earlier detection of the cellular and metabolic changes, leading to better outcomes for affected neonates.

Transthoracic ultrasound localization microscopy of myocardial vasculature in patients

Myocardial microvasculature and haemodynamics are indicative of potential microvascular diseases for patients with symptoms of coronary heart disease in the absence of obstructive coronary arteries. However, imaging microvascular structure and flow within the myocardium is challenging owing to the small size of the vessels and the constant movement of the patient's heart. Here we show the feasibility of transthoracic ultrasound localization microscopy for imaging myocardial microvasculature and haemodynamics in explanted pig hearts and in patients in vivo. Through a customized data-acquisition and processing pipeline with a cardiac phased-array probe, we leveraged motion correction and tracking to reconstruct the dynamics of microcirculation. For four patients, two of whom had impaired myocardial function, we obtained super-resolution images of myocardial vascular structure and flow using data acquired within a breath hold. Myocardial ultrasound localization microscopy may facilitate the understanding of myocardial microcirculation and the management of patients with cardiac microvascular diseases.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning

With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1-5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5-1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.

Broad Elevation Projection Super-Resolution Ultrasound (BEP-SRUS) Imaging With a 1-D Unfocused Linear Array

Super-resolution ultrasound (SRUS) through localizing spatially isolated microbubbles (MBs) has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale. However, 3-D SRUS imaging remains a challenge due to the fabrication and computational complexity of 2-D matrix array probes. Inspired by X-ray radiography which can present information within a volume in a single projection image with much simpler hardware than X-ray computerized tomography (CT), this study investigates the feasibility of broad elevation projection super-resolution (BEP-SR) ultrasound using a 1-D unfocused linear array. Both simulation and in vitro experiments were conducted on 3-D microvessel phantoms. In vivo demonstration was done on the Rabbit kidney. Data from a 1-D linear array with and without an elevational focus were synthesized by summing up row signals acquired from a 2-D matrix array with and without delays. A full 3-D reconstruction was also generated as the reference, using the same data of the 2-D matrix array but without summing row signals. Results show that using an unfocused 1-D array probe, BEP-SR can capture significantly more information within a volume in both vascular structure and flow velocity than the conventional 1-D elevational-focused probe. Compared with the 2-D projection image of the full 3-D SRUS results using the 2-D array probe with the same aperture size, the 2-D projection SRUS image of BEP-SR has similar volume coverage, using 32 folds fewer independent elements. This study demonstrates BEP-SR's ability of high-resolution imaging of microvascular structures and flow velocity within a 3-D volume at significantly reduced costs. The proposed BEP method could significantly benefit the clinical translation of the SRUS imaging technique by making it more affordable and repeatable.

Ultrasound Localization Microscopy for Breast Cancer Imaging in Patients: Protocol Optimization and Comparison with Shear Wave Elastography

OBJECTIVE

Ultrasound localization microscopy (ULM) has gained increasing attention in recent years because of its ability to visualize blood vessels at super-resolution. The field of oncology, in particular, could benefit from detailed vascular characterization, for example, for diagnosis and therapy monitoring. This study was aimed at refining ULM for breast cancer patients by optimizing the measurement protocol, identifying translational challenges and combining ULM and shear wave elastography.

METHODS

We computed ULM images of 11 patients with breast cancer by recording contrast-enhanced ultrasound (CEUS) sequences and post-processing them in an offline pipeline. For CEUS, two different doses and injection speeds of SonoVue were applied. The best injection protocol was determined based on quantitative parameters derived from so-called occurrence maps. In addition, a suitable measurement time window was determined, also considering the occurrence of motion. ULM results were compared with shear wave elastography and histological vessel density.

RESULTS

At the higher dose and injection speed, the highest number of microbubbles, number of tracks and vessel coverage were achieved, leading to the most detailed representation of tumor vasculature. Even at the highest concentration, no significant overlay of microbubble signals occurred. Motion significantly reduced the number of usable frames, thus limiting the measurement window to 3.5 min. ULM vessel coverage was comparable to the histological vessel fraction and correlated significantly with mean tumor elasticity.

CONCLUSION

The settings for microbubble injection strongly influence ULM images, thus requiring optimized protocols for different indications. Patient and examiner motion was identified as the main translational challenge for ULM.

Improved diagnostic confidence using Super Resolution CEUS imaging in testicular lesions

Ultrasound is the most used interdisciplinary non-ionizing imaging technique in clinical pathologies of the testis. The testis may be affected by a plethora of different disorders such as vasculopathies, trauma, infections and manifestations of primary and secondary malignant masses. Conventional ultrasound represents the basic imaging modality of choice to assess scrotal disorders. Contrast-enhanced ultrasound (CEUS) can provide further information to distinguish between benign and malignant testicular mass lesions. The recent introduction of Super Resolution CEUS Micro-Vascular Imaging (MVI SR) and Time of Arrival (TOA SR) parametric mapping compliments the information provided by conventional CEUS, since these two new post-processing techniques improve the visualization of microvascular structures with slow blood flow and provide high-resolution images of the peak contrast enhancement and temporal perfusion patterns. This paper gives a comprehensive overview of differential diagnoses of the testicular disorder and their corresponding sono-morphologic correlates based on representative cases of the Interdisciplinary Ultrasound Center of the University Hospital Munich.

Deep learning for fast denoising filtering in ultrasound localization microscopy

Objective.Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning.Approach.In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in bothin vitroflow phantom experiment andin vivoexperiment of New Zealand rabbit tumor.Main results.Forin vitroflow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. Forin vivoanimal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24μm and two microvessels separated by 46μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds forin vitroandin vivoexperiments were 0.041 s frame-1and 0.062 s frame-1, respectively.Significance.DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.

Fast 3D Super-Resolution Ultrasound With Adaptive Weight-Based Beamforming

OBJECTIVE

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes.

METHOD

This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo.

RESULTS

Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS.

CONCLUSION

The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments.

SIGNIFICANCE

Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

Ultrasound localization microscopy

Ultrasound Localization Microscopy (ULM) is an emerging technique that provides impressive super-resolved images of microvasculature, i.e., images with much better resolution than the conventional diffraction-limited ultrasound techniques and is already taking its first steps from preclinical to clinical applications. In comparison to the established perfusion or flow measurement methods, namely contrast-enhanced ultrasound (CEUS) and Doppler techniques, ULM allows imaging and flow measurements even down to the capillary level. As ULM can be realized as a post-processing method, conventional ultrasound systems can be used for. ULM relies on the localization of single microbubbles (MB) of commercial, clinically approved contrast agents. In general, these very small and strong scatterers with typical radii of 1-3 µm are imaged much larger in ultrasound images than they actually are due to the point spread function of the imaging system. However, by applying appropriate methods, these MBs can be localized with sub-pixel precision. Then, by tracking MBs over successive frames of image sequences, not only the morphology of vascular trees but also functional information such as flow velocities or directions can be obtained and visualized. In addition, quantitative parameters can be derived to describe pathological and physiological changes in the microvasculature. In this review, the general concept of ULM and conditions for its applicability to microvessel imaging are explained. Based on this, various aspects of the different processing steps for a concrete implementation are discussed. The trade-off between complete reconstruction of the microvasculature and the necessary measurement time as well as the implementation in 3D are reviewed in more detail, as they are the focus of current research. Through an overview of potential or already realized preclinical and clinical applications - pathologic angiogenesis or degeneration of vessels, physiological angiogenesis, or the general understanding of organ or tissue function - the great potential of ULM is demonstrated.

Super-resolution ultrasound microvascular imaging: Is it ready for clinical use?

The field of super-resolution ultrasound microvascular imaging has been rapidly growing over the past decade. By leveraging contrast microbubbles as point targets for localization and tracking, super-resolution ultrasound pinpoints the location of microvessels and measures their blood flow velocity. Super-resolution ultrasound is the first in vivo imaging modality that can image micron-scale vessels at a clinically relevant imaging depth without tissue destruction. These unique capabilities of super-resolution ultrasound provide structural (vessel morphology) and functional (vessel blood flow) assessments of tissue microvasculature on a global and local scale, which opens new doors for many enticing preclinical and clinical applications that benefit from microvascular biomarkers. The goal of this short review is to provide an update on recent advancements in super-resolution ultrasound imaging, with a focus on summarizing existing applications and discussing the prospects of translating super-resolution imaging to clinical practice and research. In this review, we also provide brief introductions of how super-resolution ultrasound works, how does it compare with other imaging modalities, and what are the tradeoffs and limitations for an audience who is not familiar with the technology.

Frame rate effects and their compensation on super-resolution microvessel imaging using ultrasound localization microscopy

Ultrasound localization microscopy (ULM) breaks the diffraction limit and allows imaging microvasculature at micrometric resolution while preserving the penetration depth. Frame rate plays an important role for high-quality ULM imaging, but there is still a lack of review and investigation of the frame rate effects on ULM. This work aims to clarify how frame rate influences the performance of ULM, including the effects of microbubble detection, localization and tracking. The performance of ULM was evaluated using an in vivo rat brain dataset (15.6 MHz, 3 tilted plane waves (-5°, 0°, +5°), at a compounded frame rate of 1000 Hz) with different frame rates. Quantification methods, including Fourier ring correlation and saturation parameter, were applied to analyze the spatial resolution and reconstruction efficiency, respectively. In addition, effects on each crucial step in ULM processing were further analyzed. Results showed that when frame rates dropped from 1000 Hz to 250 Hz, the spatial resolution deteriorated from 9.9 μm to 15.0 μm. Applying a velocity constraint was able to improve the ULM performance, but inappropriate constraint may artificially result in high apparent resolution. For the dataset, compared with the results of 1000 Hz frame rate, the velocity was underestimated at 100 Hz with 47.18% difference and the saturation was reduced from 55.00% at 1000 Hz to 43.34% at 100 Hz. Analysis showed that inadequate frame rate generated unreliable microbubble detection, localization and tracking as well as incomplete track reconstruction, resulting in the deterioration in spatial resolution, the underestimation in velocity measurement and the decrease in saturation. Finally, a guidance of determining the frame rate requirement was discussed by considering the required spatial sampling points based on vessel morphology, clutter filtering method, tracking algorithm and acquisition time, which provides indications for future clinical application of ULM method.

Velocity field estimation in transcranial small vessel using super-resolution ultrasound imaging velocimetry

Based on the diameter and position information of small vessels obtained by transcranial super-resolution imaging using 3 MHz low-frequency chirp plane waves, a Gaussian-like non-linear compression was adopted to compress the blood flow signals in spatiotemporal filtering (STF) data to a precise region, and then estimate the blood flow velocity field inside the region over the adjacent time intervals using ultrasound imaging velocimetry (UIV). Imaging parameters, such as the mechanical index (MI), frame rate, and microbubble (MB) concentration, are critical during the estimation of velocity fields over a short time at high MB contrast agent concentrations. These were optimized through experiments and algorithms, in which dividing the connected domain was proposed to calculate MB cluster spot centroid spacing (SCS) and the spot-to-flow area ratio (SFAR) to determine the suitable MB concentration. The results of the in vitro experiments showed that the estimation of the small vessel flow velocity field was consistent with the theoretical results; the velocity field resolution for vessels with diameters of 0.5 mm and 0.3 mm was 36 μm and 21 μm, and the error between the mean velocity and the theoretical value was 0.7 % and 0.67 %, respectively.

Three-Dimensional Ultrasound Localization Microscopy with Bipartite Graph-Based Microbubble Pairing and Kalman-Filtering-Based Tracking on a 256-Channel Verasonics Ultrasound System with a 32 × 32 Matrix Array

Three-dimensional (3D) ultrasound localization microscopy (ULM) using a 2-D matrix probe and microbubbles (MBs) has been recently proposed to visualize microvasculature beyond the ultrasound diffraction limit in three spatial dimensions. However, 3D ULM suffers from several limitations: (1) high system complexity due to numerous channel counts, (2) complex MB flow dynamics in 3D, and (3) extremely long acquisition time. To reduce the system complexity while maintaining high image quality, we used a sub-aperture process to reduce received channel counts. To address the second issue, a 3D bipartite graph-based method with Kalman filtering-based tracking was used in this study for MB tracking. An MB separation approach was incorporated to separate high concentration MB data into multiple, sparser MB datasets, allowing better MB localization and tracking for a limited acquisition time. The proposed method was first validated in a flow channel phantom, showing improved spatial resolutions compared with the contrasted enhanced power Doppler image. Then the proposed method was evaluated with an in vivo chicken embryo brain dataset. Results showed that the reconstructed 3D super-resolution image achieved a spatial resolution of around 52 μm (smaller than the wavelength of around 200 μm). Microvessels that cannot be resolved clearly using localization only, can be well identified with the tailored 3D pairing and tracking algorithms. To sum up, the feasibility of the 3D ULM is shown, indicating the great possibility in clinical applications.

Anatomic and Functional Imaging Using Row-Column Arrays

Row-column (RC) arrays have the potential to yield full 3-D ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review article summarizes the current literature for RC imaging and demonstrates that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three orthogonal planes to be beamformed at 30 Hz, providing near real-time imaging ideal for positioning the probe and improving the operator's workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super-resolution imaging (SRI) with isotropic resolution below [Formula: see text]. RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays.

Super-Resolution Ultrasound Through Sparsity-Based Deconvolution and Multi-Feature Tracking

Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo. Currently, it is still challenging to achieve high accuracy in localisation and tracking particularly with limited imaging frame rates and in the presence of high bubble concentrations. This study introduces microbubble image features into a Kalman tracking framework, and makes the framework compatible with sparsity-based deconvolution to address these key challenges. The performance of the method is evaluated on both simulations using individual bubble signals segmented from in vivo data and experiments on a mouse brain and a human lymph node. The simulation results show that the deconvolution not only significantly improves the accuracy of isolating overlapping bubbles, but also preserves some image features of the bubbles. The combination of such features with Kalman motion model can achieve a significant improvement in tracking precision at a low frame rate over that using the distance measure, while the improvement is not significant at the highest frame rate. The in vivo results show that the proposed framework generates SR images that are significantly different from the current methods with visual improvement, and is more robust to high bubble concentrations and low frame rates.

Ultrasound super-resolution imaging with a hierarchical Kalman tracker

Microbubble (MB) tracking plays an important role in ultrasound super-resolution imaging (SRI) by enabling velocity estimation and improving image quality. This work presents a new hierarchical Kalman (HK) tracker to achieve better performance at scenarios with high concentrations of MBs and high localization uncertainty. The method attempts to follow MBs with different velocity ranges using different Kalman filters. An extended simulation framework for evaluating trackers is also presented and used for comparison of the proposed HK tracker with the nearest-neighbor (NN) and Kalman (K) trackers. The HK tracks were most similar to the ground truth with the highest Jaccard similarity coefficient in 79% of the scenarios and the lowest root-mean-square error in 72% of the scenarios. The HK tracker reconstructed vessels with a more accurate diameter. In a scenario with an uncertainty of 51.2μm in MB localization, a vessel diameter of 250μm was estimated as 257μm by HK tracker, compared with 329μm and 389μm for the K and NN trackers. In the same scenario, the HK tracker estimated MB velocities with a relative bias down to 1.7% and a relative standard deviation down to 8.3%. Finally, the different tracking techniques were applied to in vivo data from rat kidneys, and trends similar to the simulations were observed. Conclusively, the results showed an improvement in tracking performance, when the HK tracker was employed in comparison with the NN and K trackers.

Neural Network Kalman Filtering for 3-D Object Tracking From Linear Array Ultrasound Data

Many interventional surgical procedures rely on medical imaging to visualize and track instruments. Such imaging methods not only need to be real time capable but also provide accurate and robust positional information. In ultrasound (US) applications, typically, only 2-D data from a linear array are available, and as such, obtaining accurate positional estimation in three dimensions is nontrivial. In this work, we first train a neural network, using realistic synthetic training data, to estimate the out-of-plane offset of an object with the associated axial aberration in the reconstructed US image. The obtained estimate is then combined with a Kalman filtering approach that utilizes positioning estimates obtained in previous time frames to improve localization robustness and reduce the impact of measurement noise. The accuracy of the proposed method is evaluated using simulations, and its practical applicability is demonstrated on experimental data obtained using a novel optical US imaging setup. Accurate and robust positional information is provided in real time. Axial and lateral coordinates for out-of-plane objects are estimated with a mean error of 0.1 mm for simulated data and a mean error of 0.2 mm for experimental data. The 3-D localization is most accurate for elevational distances larger than 1 mm, with a maximum distance of 6 mm considered for a 25-mm aperture.

Ultrasound Localization Microscopy in Liquid Metal Flows

Liquid metal convection plays an important role in natural and technical processes. In experimental studies, an instrumentation with a sub-millimeter spatial resolution is required in an opaque fluid to resolve the flow field near the boundary layer. Using ultrasound methods, the trade-off between the frequency and imaging depth of typical laboratory experiments limits the spatial resolution. Therefore, the method of ultrasound localization microscopy (ULM) was introduced in liquid metal experiments for the first time in this study. To isolate the intrinsic scattering particles, an adaptive nonlinear beamformer was applied. As a result, an average spatial resolution of 188 μm could be achieved, which corresponded to a fraction of the ultrasound wavelength of 0.28. A convection experiment was measured using ULM. Due to the increased spatial resolution, the high-velocity gradients and the recirculation areas of a liquid metal convection experiment could be observed for the first time. The presented technique paves the way for in-depth flow studies of convective turbulent liquid metal flows that are close to the boundary layer.

[Super-resolution ultrasound imaging : Methods and applications]

CLINICAL/METHODICAL ISSUE

The microvasculature plays an important role in many pathologic conditions but cannot be characterized in high resolution via conventional ultrasound methods.

STANDARD RADIOLOGICAL METHODS

Doppler-based techniques, contrast-enhanced sonography as well as dynamic contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used to characterize tissue vascularization. However, these techniques cannot visualize the microvasculature adequately.

METHODICAL INNOVATION

Ultrasound localization microscopy (ULM) consists of contrast-enhanced ultrasound measurements in combination with a complex post-processing algorithm which detects microbubbles with high precision. The vasculature can then be visualized by accumulating the microbubble positions in a final image.

PERFORMANCE

Compared to conventional ultrasound techniques, ULM improves the image resolution by a factor of more than 10. This currently results in resolutions down to 10 µm and allows, therefore, the visualization of capillaries and the assessment of their perfusion. Also, this does not lead to a reduction of the penetration depth or the signal-to-noise ratio (SNR).

ACHIEVEMENT

The method enables the visualization of vascular structures in unsurpassed detail and has the potential to offer new possibilities for the diagnosis of various diseases and for gaining insights into physiological processes. However, ULM is not commercially available yet but is intensely being tested in clinical studies.

PRACTICAL RECOMMENDATIONS

ULM could potentially be applied to all fields in which the vasculature is relevant. Current fields of application include oncology, nephrology, and neurological research.

Cerebral microcirculation mapped by echo particle tracking velocimetry quantifies the intracranial pressure and detects ischemia

Affecting 1.1‰ of infants, hydrocephalus involves abnormal accumulation of cerebrospinal fluid, resulting in elevated intracranial pressure (ICP). It is the leading cause for brain surgery in newborns, often causing long-term neurologic disabilities or even death. Since conventional invasive ICP monitoring is risky, early neurosurgical interventions could benefit from noninvasive techniques. Here we use clinical contrast-enhanced ultrasound (CEUS) imaging and intravascular microbubble tracking algorithms to map the cerebral blood flow in hydrocephalic pediatric porcine models. Regional microvascular perfusions are quantified by the cerebral microcirculation (CMC) parameter, which accounts for the concentration of micro-vessels and flow velocity in them. Combining CMC with hemodynamic parameters yields functional relationships between cortical micro-perfusion and ICP, with correlation coefficients exceeding 0.85. For cerebral ischemia cases, the nondimensionalized cortical micro-perfusion decreases by an order of magnitude when ICP exceeds 50% of the MAP. These findings suggest that CEUS-based CMC measurement is a plausible noninvasive method for assessing the ICP and detecting ischemia.

In Vivo Ultrasound Localization Microscopy Imaging of the Kidney's Microvasculature With Block-Matching 3-D Denoising

Structural abnormalities and functional changes of renal microvascular networks play a significant pathophysiologic role in the occurrence of kidney diseases. Super-resolution ultrasound imaging has been successfully utilized to visualize the microvascular network and provide valuable diagnostic information. To prevent the burst of microbubbles, a lower mechanical index (MI) is generally used in ultrasound localization microscopy (ULM) imaging. However, high noise levels lead to incorrect signal localizations in relatively low-MI settings and deep tissue. In this study, we implemented a block-matching 3-D (BM3D) image-denoising method, after the application of singular value decomposition filtering, to further suppress the noise at various depths. The in vitro flow-phantom results show that the BM3D method helps the significant reduction of the error localizations, thus improving the localization accuracy. In vivo rhesus macaque experiments help conclude that the BM3D method improves the resolution more than other image-based denoising techniques, such as the nonlocal means method. The obtained clutter-filtered images with fewer incorrect localizations can enable robust ULM imaging, thus helping in establishing an effective diagnostic tool.

Sentinel lymph node detection by combining nonradioactive techniques with contrast agents: State of the art and prospects

The status of sentinel lymph nodes (SLNs) has a substantial prognostic value because these nodes are the first place where cancer cells accumulate along their spreading route. Routine SLN biopsy ("gold standard") involves peritumoral injections of radiopharmaceuticals, such as technetium-99m, which has obvious disadvantages. This review examines the methods used as "gold standard" analogs to diagnose SLNs. Nonradioactive preoperative and intraoperative methods of SLN detection are analyzed. Promising photonic tools for SLNs detection are reviewed, including NIR-I/NIR-II fluorescence imaging, photoswitching dyes for SLN detection, in vivo photoacoustic detection, imaging and biopsy of SLNs. Also are discussed methods of SLN detection by magnetic resonance imaging, ultrasonic imaging systems including as combined with photoacoustic imaging, and methods based on the magnetometer-aided detection of superparamagnetic nanoparticles. The advantages and disadvantages of nonradioactive SLN-detection methods are shown. The review concludes with prospects for the use of conservative diagnostic methods in combination with photonic tools.

Study on the Application of Super-Resolution Ultrasound for Cerebral Vessel Imaging in Rhesus Monkeys

Background: Ultrasound is ideal for displaying intracranial great vessels but not intracranial microvessels and terminal vessels. Even with contrast agents, the imaging effect is still unsatisfactory. In recent years, significant theoretical advances have been achieved in super-resolution imaging. The latest commonly used ultrafast plane-wave ultrasound Doppler imaging of the brain and microbubble-based super-resolution ultrasound imaging have been applied to the imaging of cerebral microvessels and blood flow in small animals such as mice but have not been applied to in vivo imaging of the cerebral microvessels in monkeys and larger animals. In China, preliminary research results have been obtained using super-resolution imaging in certain fields but rarely in fundamental and clinical experiments on large animals. In recent years, we have conducted a joint study with the Xi'an Jiaotong University to explore the application and performance of this new technique in the diagnosis of cerebrovascular diseases in large animals.

Objective: To explore the characteristics and advantages of microbubble-based super-resolution ultrasound imaging of intracranial vessels in rhesus monkeys compared with conventional transcranial ultrasound.

Methods: First, the effectiveness and feasibility of the super-resolution imaging technique were verified by modular simulation experiments. Then, the imaging parameters were adjusted based on in vitro experiments. Finally, two rhesus monkeys were used for in vivo experiments of intracranial microvessel imaging.

Results: Compared with conventional plane-wave imaging, super-resolution imaging could measure the inner diameters of cerebral microvessels at a resolution of 1 mm or even 0.7 mm and extract blood flow information. In addition, it has a better signal-to-noise ratio (5.625 dB higher) and higher resolution (~30-fold higher). The results of the experiments with rhesus monkeys showed that microbubble-based super-resolution ultrasound imaging can achieve an optimal resolution at the micron level and an imaging depth >35 mm.

Conclusion: Super-resolution imaging can realize the monitoring imaging of high-resolution and fast calculation of microbubbles in the process of tissue damage, providing an important experimental basis for the clinical application of non-invasive transcranial ultrasound.

Measuring Image Resolution in Ultrasound Localization Microscopy

The resolution of an imaging system is usually determined by the width of its point spread function and is measured using the Rayleigh criterion. For most system, it is in the order of the imaging wavelength. However, super resolution techniques such as localization microscopy in optical and ultrasound imaging can resolve features an order of magnitude finer than the wavelength. The classical description of spatial resolution no longer applies and new methods need to be developed. In optical localization microscopy, the Fourier Ring Correlation has showed to be an effective and practical way to estimate spatial resolution for Single Molecule Localization Microscopy data. In this work, we wish to investigate how this tool can provide a direct and universal estimation of spatial resolution in Ultrasound Localization Microscopy. Moreover, we discuss the concept of spatial sampling in Ultrasound Localization Microscopy and demonstrate how the Nyquist criterion for sampling drives the spatial/temporal resolution tradeoff. We measured spatial resolution on five different datasets over rodent's brain, kidney and tumor finding values between [Formula: see text] and [Formula: see text] for precision of localization between [Formula: see text] and [Formula: see text]. Eventually, we discuss from those in vivo datasets how spatial resolution in Ultrasound Localization Microscopy depends on both the localization precision and the total number of detected microbubbles. This study aims to offer a practical and theoretical framework for image resolution in Ultrasound Localization Microscopy.

Characterization of Anti-angiogenic Chemo-sensitization via Longitudinal Ultrasound Localization Microscopy in Colorectal Carcinoma Tumor Xenografts

OBJECTIVE

Super-resolution ultrasound localization microscopy (ULM) has unprecedented vascular resolution at clinically relevant imaging penetration depths. This technology can potentially screen for the transient microvascular changes that are thought to be critical to the synergistic effect(s) of combined chemotherapy-antiangiogenic agent regimens for cancer.

METHODS

In this paper, we apply this technology to a high-throughput colorectal carcinoma xenograft model treated with either the antiangiogenic agent sorafenib, FOLFOX-6 chemotherapy, a combination of the two treatments, or vehicle control.

RESULTS

Longitudinal ULM demonstrated morphological changes in the antiangiogenic treated cohorts, and evidence of vascular disruption caused by chemotherapy. Gold-standard histological measurements revealed reduced levels of hypoxia in the sorafenib treated cohort for both of the human cell lines tested (HCT-116 and HT-29). Therapy resistance was associated with an increase in tumor vascular fractal dimension as measured by a box-counting technique on ULM images.

CONCLUSION

These results imply that the morphological changes evident on ULM signify a functional change in the tumor microvasculature, which may be indicative of chemo-sensitivity.

SIGNIFICANCE

ULM provides additional utility for tumor therapy response evaluation by offering a myriad of morphological and functional quantitative indices for gauging treatment effect(s).

In Vivo Motion Correction in Super-Resolution Imaging of Rat Kidneys

Super-resolution (SR) imaging has the potential of visualizing the microvasculature down to the 10- [Formula: see text] level, but motion induced by breathing, heartbeats, and muscle contractions are often significantly above this level. This article, therefore, introduces a method for estimating tissue motion and compensating for this. The processing pipeline is described and validated using Field II simulations of an artificial kidney. In vivo measurements were conducted using a modified bk5000 research scanner (BK Medical, Herlev, Denmark) with a BK 9009 linear array probe employing a pulse amplitude modulation scheme. The left kidney of ten Sprague-Dawley rats was scanned during open laparotomy. A 1:10 diluted SonoVue contrast agent (Bracco, Milan, Italy) was injected through a jugular vein catheter at 100 [Formula: see text]/min. Motion was estimated using speckle tracking and decomposed into contributions from the heartbeats, breathing, and residual motion. The estimated peak motions and their precisions were: heart: axial- [Formula: see text] and lateral- [Formula: see text], breathing: axial- [Formula: see text] and lateral- [Formula: see text], and residual: axial-30 [Formula: see text] and lateral-90 [Formula: see text]. The motion corrected microbubble tracks yielded SR images of both bubble density and blood vector velocity. The estimation was, thus, sufficiently precise to correct shifts down to the 10- [Formula: see text] capillary level. Similar results were found in the other kidney measurements with a restoration of resolution for the small vessels demonstrating that motion correction in 2-D can enhance SR imaging quality.

Super-Resolution Ultrasound Localization Microscopy on a Rabbit Liver VX2 Tumor Model: An Initial Feasibility Study

Ultrasound localization microscopy can image microvasculature in vivo without sacrificing imaging penetration depth. However, the reliance on super-resolution inference limits the applicability of the technique because subpixel tissue motion can corrupt microvascular reconstruction. Consequently, the majority of previous pre-clinical research on this super-resolution procedure has been restricted to low-motion experimental models with ample motion correction or data rejection, which precludes the imaging of organ sites that exhibit a high degree of respiratory and other motion. In this article, we present a novel anesthesia protocol in rabbits that induces safe, controllable periods of apnea to enable the long image-acquisition times required for ultrasound localization microscopy. We apply this protocol to a VX2 liver tumor model undergoing sorafenib therapy and compare the results to super-resolution images from conventional high-dose isoflurane anesthesia. We find that the apneic protocol was necessary to correctly identify the poorly vascularized tumor cores, as verified by immunohistochemistry, and to reveal the tumoral microvascular architecture.

Super-resolution ultrasound localization microscopy based on a high frame-rate clinical ultrasound scanner: an in-human feasibility study

Non-invasive detection of microvascular alterations in deep tissuesin vivoprovides critical information for clinical diagnosis and evaluation of a broad-spectrum of pathologies. Recently, the emergence of super-resolution ultrasound localization microscopy (ULM) offers new possibilities for clinical imaging of microvasculature at capillary level. Currently, the clinical utility of ULM on clinical ultrasound scanners is hindered by the technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and compromised tracking performance associated with low imaging frame-rate. Here we present a robust in-human ULM on a high frame-rate (HFR) clinical ultrasound scanner to achieve super-resolution microvessel imaging using a short acquisition time (<10 s). Ultrasound MB data were acquired from different human tissues, including a healthy liver and a diseased liver with acute-on-chronic liver failure, a kidney, a pancreatic tumor, and a breast mass using an HFR clinical scanner. By leveraging the HFR and advanced processing techniques including sub-pixel motion registration, MB signal separation, and Kalman filter-based tracking, MBs can be robustly localized and tracked for ULM under the circumstances of relatively high MB concentration associated with standard clinical MB administration and limited data acquisition time in humans. Subtle morphological and hemodynamic information in microvasculature were shown based on data acquired with single breath-hold and free-hand scanning. Compared with contrast-enhanced power Doppler generated based on the same MB dataset, ULM showed a 5.7-fold resolution improvement in a vessel based on a linear transducer, and provided a wide-range blood flow speed measurement that is Doppler angle-independent. Microvasculatures with complex hemodynamics can be well-differentiated at super-resolution in both normal and pathological tissues. This preliminary study implemented the ultrafast in-human ULM in various human tissues based on a clinical scanner that supports HFR imaging, indicating the potentials of the technique for various clinical applications. However, rigorous validation of the technique in imaging human microvasculature (especially for those tiny vessel structure), preferably with a gold standard, is still required.

Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network

Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133μm to 35μm, which is smaller than the ultrasound wavelength (73μm). The proposed method was further tested in anin vivohuman liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170μm (FWHM). Adjacent microvessels with a distance of 670μm, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.

Super-Resolution Ultrasound Localization Microscopy Through Deep Learning

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultrasound imaging data. This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios, learning the nonlinear image-domain implications of overlapping RF signals originating from such sets of closely spaced microbubbles. Deep-ULM is trained effectively using realistic on-line synthesized data, enabling robust inference in-vivo under a wide variety of imaging conditions. We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is suitable for real-time applications, resolving about 70 high-resolution patches ( 128×128 pixels) per second on a standard PC. Exploiting GPU computation, this number increases to 1250 patches per second.

Ultrasound Super-Resolution Flow Measurement of Suspensions in Narrow Channels

Zinc-air flow batteries provide a scalable and cost-efficient energy storage solution. However, the achieved power density depends on the local flow conditions of the zinc particle suspension in the electrochemical cell. Numerical modeling is challenging due to the complex multiphase fluid and the interaction of flow and electrochemistry. Hence, performing experiments is crucial to investigate the influence of the flow conditions on the electrical performance, which requires flow instrumentation for the opaque suspension. To resolve the flow field across the 2.6-mm-wide flow channel of the investigated zinc-air flow battery (ZAB), a spatial resolution below 100 [Formula: see text] has to be typically achieved. Using ultrasound techniques, the achieved spatial resolution is limited by the trade-off between ultrasound frequency and imaging depth. This trade-off is even more critical for suspensions due to the scattering of the ultrasound, which increases strongly with frequency. We propose super-resolution particle tracking velocimetry (SRPTV) to overcome this limitation by achieving the required spatial resolution at a low ultrasound frequency. SRPTV is based on the super-resolution technique ultrasound localization microscopy, which is adapted to strongly scattering suspensions by using a dual-frequency-phased array and applying a coherence weighting beamformer to suppress speckles, which result from the scattering at the zinc particles of the suspension. The spatial resolution and the velocity uncertainty are characterized through calibration measurement and numerical simulation. A spatial resolution of 66 [Formula: see text] at an excitation wavelength of 330 [Formula: see text] was achieved, which is sufficient for performing flow investigation in an operational ZAB. The measured flow profile reveals shear-thinning properties and wall slip and therefore differs significantly from a parabolic flow profile of a Newtonian fluid. The presented technique offers potential for performing flow investigations of suspensions in small geometries with a spatial resolution beyond the diffraction limit.

Real-Time Magnetic Navigation of a Rotating Colloidal Microswarm Under Ultrasound Guidance

OBJECTIVE

Untethered microrobots hold great promise for applications in biomedical field including targeted delivery, biosensing, and microsurgery. A major challenge of using microrobots to perform in vivo tasks is the real-time localization and motion control using medical imaging technologies. Here we report real-time magnetic navigation of a paramagnetic nanoparticle-based microswarm under ultrasound guidance.

METHODS

A three-axis Helmholtz electromagnetic coil system integrated with an ultrasound imaging system is developed for generation, actuation, and closed-loop control of the microswarm. The magnetite nanoparticle-based microswarm is generated and navigated using rotating magnetic fields. In order to localize the microswarm in real time, the dynamic imaging contrast has been analyzed and exploited in image process to increase the signal-to-noise ratio. Moreover, imaging of the microswarm at different depths are experimentally studied and analyzed, and the minimal dose of nanoparticles for localizing a microswarm at different depths is ex vivo investigated. For real-time navigating the microswarm in a confined environment, a PI control scheme is designed.

RESULTS

Image differencing-based processing increases the signal-to-noise ratio, and the microswarm can be ex vivo localized at depth of 2.2-7.8 cm. Experimental results show that the microswarm is able to be real-time navigated along a planned path in a channel, and the average steady-state error is 0.27 mm (  ∼ 33.7% of the body length).

SIGNIFICANCE

The colloidal microswarm is real-time localized and navigated using ultrasound feedback, which shows great potential for biomedical applications that require real-time noninvasive tracking.

Kalman Filter-Based Microbubble Tracking for Robust Super-Resolution Ultrasound Microvessel Imaging

Contrast microbubble (MB)-based super-resolution ultrasound microvessel imaging (SR-UMI) overcomes the compromise in conventional ultrasound imaging between spatial resolution and penetration depth and has been successfully applied to a wide range of clinical applications. However, clinical translation of SR-UMI remains challenging due to the limited number of MBs detected within a given accumulation time. Here, we propose a Kalman filter-based method for robust MB tracking and improved blood flow speed measurement with reduced numbers of MBs. An acceleration constraint and a direction constraint for MB movement were developed to control the quality of the estimated MB trajectory. An adaptive interpolation approach was developed to inpaint the missing microvessel signal based on the estimated local blood flow speed, facilitating more robust depiction of microvasculature with a limited amount of MBs. The proposed method was validated on an ex ovo chorioallantoic membrane and an in vivo rabbit kidney. Results demonstrated improved imaging performance on both microvessel density maps and blood flow speed maps. With the proposed method, the percentage of microvessel filling in a selected blood vessel at a given accumulation period was increased from 28.17% to 74.45%. A similar SR-UMI performance was achieved with MB numbers reduced by 85.96%, compared to that with the original MB number. The results indicate that the proposed method substantially improves the robustness of SR-UMI under a clinically relevant imaging scenario where SR-UMI is challenged by a limited MB accumulation time, reduced number of MBs, lowered imaging frame rate, and degraded signal-to-noise ratio.

From Anatomy to Functional and Molecular Biomarker Imaging and Therapy: Ultrasound Is Safe, Ultrafast, Portable, and Inexpensive

Ultrasound is the most widely used medical imaging modality worldwide. It is abundant, extremely safe, portable, and inexpensive. In this review, we consider some of the current development trends for ultrasound imaging, which build upon its current strength and the popularity it experiences among medical imaging professional users.Ultrasound has rapidly expanded beyond traditional radiology departments and cardiology practices. Computing power and data processing capabilities of commonly available electronics put ultrasound systems in a lab coat pocket or on a user's mobile phone. Taking advantage of new contributions and discoveries in ultrasound physics, signal processing algorithms, and electronics, the performance of ultrasound systems and transducers have progressed in terms of them becoming smaller, with higher imaging performance, and having lower cost. Ultrasound operates in real time, now at ultrafast speeds; kilohertz frame rates are already achieved by many systems.Ultrasound has progressed beyond anatomical imaging and monitoring blood flow in large vessels. With clinical approval of ultrasound contrast agents (gas-filled microbubbles) that are administered in the bloodstream, tissue perfusion studies are now routine. Through the use of modern ultrasound pulse sequences, individual microbubbles, with subpicogram mass, can be detected and observed in real time, many centimeters deep in the body. Ultrasound imaging has broken the wavelength barrier; by tracking positions of microbubbles within the vasculature, superresolution imaging has been made possible. Ultrasound can now trace the smallest vessels and capillaries, and obtain blood velocity data in those vessels.Molecular ultrasound imaging has now moved closer to clinic; the use of microbubbles with a specific affinity to endothelial biomarkers allows selective accumulation and retention of ultrasound contrast in the areas of ischemic injury, inflammation, or neoangiogenesis. This will aid in noninvasive molecular imaging and may provide additional help with real-time guidance of biopsy, surgery, and ablation procedures.The ultrasound field can be tightly focused inside the body, many centimeters deep, with millimeter precision, and ablate lesions by energy deposition, with thermal or mechanical bioeffects. Some of such treatments are already in clinical use, with more indications progressing through the clinical trial stage. In conjunction with intravascular microbubbles, focused ultrasound can be used for tissue-specific drug delivery; localized triggered release of sequestered drugs from particles in the bloodstream may take time to get to clinic. A combination of intravascular microbubbles with circulating drug and low-power ultrasound allows transient opening of vascular endothelial barriers, including blood-brain barrier; this approach has reached clinical trial stage. Therefore, the drugs that normally would not be getting to the target tissue in the brain will now have an opportunity to produce therapeutic efficacy.Overall, medical ultrasound is developing at a brisk rate, even in an environment where other imaging modalities are also advancing rapidly and may be considered more lucrative. With all the current advances that we discuss, and many more to come, ultrasound may help solve many problems that modern medicine is facing.

Assessing Vessel Reconstruction in Ultrasound Localization Microscopy by Maximum Likelihood Estimation of a Zero-Inflated Poisson Model

In clinical applications of super-resolution ultrasound imaging, it is often not possible to achieve a full reconstruction of the microvasculature within a limited measurement time. This makes the comparison of studies and quantitative parameters of vascular morphology and perfusion difficult. Therefore, saturation models were proposed to predict adequate measurement times and estimate the degree of vessel reconstruction. Here, we derive a statistical model for the microbubble counts in super-resolution voxels by a zero-inflated Poisson (ZIP) process. In this model, voxels either belong to vessels with probability P_v and count events with Poisson rate Λ , or they are empty and remain zero. In this model, P_v represents the vessel voxel density in the super-resolution image after infinite measurement time. For the parameters P_v and Λ , we give Cramér-Rao lower bounds (CRLBs) for the estimation variance and derive maximum likelihood estimators (MLEs) in a novel closed-form solution. These can be calculated with knowledge of only the counts at the end of the acquisition time. The estimators are applied to preclinical and clinical data and the MLE outperforms alternative estimators proposed before. The estimated degree of reconstruction lies between 38% and 74% after less than 90 s. Vessel probability P_v ranged from 4% to 20%. The rate parameter Λ was estimated in the range of 0.5-1.3 microbubbles/voxel. For these parameter ranges, the CRLB gives standard deviations of less than 2%, which supports that the parameters can be estimated with good precision already for limited acquisition times.

Ultrasound Microvascular Imaging Based on Super-Resolution Radial Fluctuations

OBJECTIVES

Super-resolution ultrasound (SRUS) has become a tool for in vivo microvascular imaging. Most of the SRUS methods are based on microbubble localization: namely, ultrasound localization microscopy (ULM). The aim of this study was to develop a nonlocalization SRUS method and verify its feasibility in microvascular imaging.

METHODS

We introduce a new super-resolution strategy based on the postprocessing of contrast-enhanced ultrasound. The proposed method, which is termed ultrasound diffraction attenuation microscopy (UDAM), uses super-resolution radial fluctuations instead of microbubble localization to overcome acoustic diffraction limits. Biceps of Japanese long-ear white rabbits were adopted to validate its feasibility on muscle vascular imaging, using a clinical accessible ultrasound system at a frame rate of 30 Hz under a single bolus injection of SonoVue (Bracco SpA, Milan, Italy). The super-resolution image was compared with the maximum-intensity projection and ULM.

RESULTS

The animal study illustrates that the proposed UDAM can obtain super-resolution microvascular images of rabbits' muscles under a single bolus injection of SonoVue with a 150-second contrast-enhanced ultrasound video. Both ULM and UDAM can achieve a very similar vascular structure with the maximum-intensity projection but much higher spatial resolution. The measurement of 1-dimensional signals shows that UDAM can distinguish the subwavelength structures and substantial reduce the full width at half-maximum of microvessels.

CONCLUSIONS

We conclude UDAM provides a noninvasive tool for in vivo super-resolution microvascular imaging.