In vivo acoustic super-resolution and super-resolved velocity mapping using microbubbles

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 Microscopy: Shedding New Light on In Vivo Imaging

Over the past two decades, super-resolution microscopy (SRM), which offered a significant improvement in resolution over conventional light microscopy, has become a powerful tool to visualize biological activities in both fixed and living cells. However, completely understanding biological processes requires studying cells in a physiological context at high spatiotemporal resolution. Recently, SRM has showcased its ability to observe the detailed structures and dynamics in living species. Here we summarized recent technical advancements in SRM that have been successfully applied to in vivo imaging. Then, improvements in the labeling strategies are discussed together with the spectroscopic and chemical demands of the fluorophores. Finally, we broadly reviewed the current applications for super-resolution techniques in living species and highlighted some inherent challenges faced in this emerging field. We hope that this review could serve as an ideal reference for researchers as well as beginners in the relevant field of in vivo super resolution imaging.

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.

3D printed calibration micro-phantoms for super-resolution ultrasound imaging validation

This study evaluates the use of 3D printed phantoms for 3D super-resolution ultrasound imaging (SRI) algorithm calibration. The main benefit of the presented method is the ability to do absolute 3D micro-positioning of sub-wavelength sized ultrasound scatterers in a material having a speed of sound comparable to that of tissue. Stereolithography is used for 3D printing soft material calibration micro-phantoms containing eight randomly placed scatterers of nominal size 205 μm × 205 μm × 200 μm. The backscattered pressure spatial distribution is evaluated to show similar distributions from micro-bubbles as the 3D printed scatterers. The printed structures are found through optical validation to expand linearly in all three dimensions by 2.6% after printing. SRI algorithm calibration is demonstrated by imaging a phantom using a λ/2 pitch 3 MHz 62+62 row-column addressed (RCA) ultrasound probe. The printed scatterers will act as point targets, as their dimensions are below the diffraction limit of the ultrasound system used. Two sets of 640 volumes containing the phantom features are imaged, with an intervolume uni-axial movement of the phantom of 12.5 μm, to emulate a flow velocity of 2 mm/s at a frame rate of 160 Hz. The ultrasound signal is passed to a super-resolution pipeline to localise the positions of the scatterers and track them across the 640 volumes. After compensating for the phantom expansion, a scaling of 0.989 is found between the distance between the eight scatterers calculated from the ultrasound data and the designed distances. The standard deviation of the variation in the scatterer positions along each track is used as an estimate of the precision of the super-resolution algorithm, and is expected to be between the two limiting estimates of (σ̃_x,σ̃_y,σ̃_z) = (22.7 μm, 27.6 μm, 9.7 μm) and (σ̃_x,σ̃_y,σ̃_z) = (18.7 μm, 19.3 μm, 8.9 μm). In conclusion, this study demonstrates the use of 3D printed phantoms for determining the accuracy and precision of volumetric super-resolution algorithms.

Single laser-shot super-resolution photoacoustic tomography with fast sparsity-based reconstruction

Recently, ℓ 1 -norm based reconstruction approaches have been used with linear array systems to improve photoacoustic resolution and demonstrate undersampled imaging when there is sufficient sparsity in some domain. However, such approaches have yet to beat the half-wavelength resolution limit. In this paper, the ability to beat the half-wavelength diffraction limit is demonstrated using a 5 MHz ring array photoacoustic tomography system and ℓ 1 -norm based reconstruction approaches. We used the array system to image wire targets at ≈ 2 - 3 cm depth in both intralipid scattering solution and water. The minimum observable separation was estimated as 70 ± 10 μ m , improving on the half-wavelength resolution limit of 145 μ m . This improvement was demonstrated even when using a random projection transform to reduce data by 99 % , enabling substantially faster reconstruction times. This is the first photoacoustic tomography approach capable of beating the half-wavelength resolution limit with a single laser shot.

Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound

Generation of super-resolution (SR) ultrasound (US) images, created from the successive localization of individual microbubbles in the circulation, has enabled the visualization of microvascular structure and flow at a level of detail that was not possible previously. Despite rapid progress, tradeoffs between spatial and temporal resolution may challenge the translation of this promising technology to the clinic. To temper these tradeoffs, we propose a method based on morphological image reconstruction. This method can extract from ultrafast contrast-enhanced US (CEUS) images hundreds of microbubble peaks per image (312-by-180 pixels) with intensity values varying by an order of magnitude. Specifically, it offers a fourfold increase in the number of peaks detected per frame, requires on the order of 100 ms for processing, and is robust to additive electronic noise (down to 3.6-dB CNR in CEUS images). By integrating this method to an SR framework, we demonstrate a sixfold improvement in spatial resolution, when compared with CEUS, in imaging chicken embryo microvessels. This method that is computationally efficient and, thus, scalable to large data sets may augment the abilities of SR-US in imaging microvascular structure and function.

Structural and Functional Imaging of the Retina in Central Retinal Artery Occlusion - Current Approaches and Future Directions

Central retinal artery occlusion (CRAO) is a form of acute ischemic stroke which affects the retina. Intravenous thrombolysis is emerging as a compelling therapeutic approach. However, it is not known which patients may benefit from this therapy because there are no imaging modalities that adequately distinguish viable retina from irreversibly infarcted retina. The inner retina receives arterial supply from the central retinal artery and there is robust collateralization between this circulation and the outer retinal circulation, provided by the posterior ciliary circulation. Fundus photography can show canonical changes associated with CRAO including a cherry-red spot, arteriolar boxcarring and retinal pallor. Fluorescein angiography provides 2-dimensional imaging of the retinal circulation and can distinguish a complete from a partial CRAO as well as central versus peripheral retinal non-perfusion. Transorbital ultrasonography may assay flow through the central retinal artery and is useful in the exclusion of other orbital pathology that can mimic CRAO. Optical coherence tomography provides structural information on the different layers of the retina and exploratory work has described its utility in determining the time since onset of ischemia. Two experimental techniques are discussed. 1) Retinal functional imaging permits generation of capillary perfusion maps and can assay retinal oxygenation and blood flow velocity. 2) Photoacoustic imaging combines the principles of optical excitation and ultrasonic detection and - in animal studies - has been used to determine the retinal oxygen metabolic rate. Future techniques to determine retinal viability in clinical practice will require rapid, easily used, and reproducible methods that can be deployed in the emergency setting.

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which leads to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network (CNN) based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo in a rat brain. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 μ m with an improvement in resolution when compared against a conventional approach.

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculaturein vivowith a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conductedin silicousing the SIMUS simulation software in an anatomically realistic phantom and then compared toin vivoacquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6μm and 9.93μm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.

Three-dimensional visualization and improved quantification with super-resolution ultrasound imaging - validation framework for analysis of microvascular morphology using a chicken embryo model

The purpose of this study was to improve the morphological analysis of microvascular networks depicted in three-dimensional (3D) super-resolution ultrasound (SR-US) images. This was supported by qualitative and quantitative validation by comparison to matched brightfield microscopy and traditional B-mode ultrasound (US) images. Contrast-enhanced US (CEUS) images were collected using a preclinical US scanner (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX250 linear array transducer. CEUS imaging was performed after administration of a microbubble (MB) contrast agent into the vitelline network of a developing chicken embryo. Volume data was collected by mechanically scanning the US transducer throughout a tissue volume-of-interest in 90μm step increments. CEUS images were collected at each increment and stored as in-phase/quadrature data (2000 frames at 152 frames per sec). SR-US images were created for each cross-sectional plane using established data processing methods. All SR-US images were then used to reconstruct a final 3D volume for vessel diameter (VD) quantification and for surface rendering. VD quantification from the 3D SR-US data exhibited an average error of 6.1% ± 6.0% when compared with matched brightfield microscopy images, whereas measurements from B-mode US images had an average error of 77.1% ± 68.9%. Volume and surface renderings in 3D space enabled qualitative validation and improved visualization of small vessels below the axial resolution of the US system. Overall, 3D SR-US image reconstructions depicted the microvascular network of the developing chicken embryos. Improved visualization of isolated vessels and quantification of microvascular morphology from SR-US images achieved a considerably greater accuracy compared to B-mode US measurements.

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.

Current Development and Applications of Super-Resolution Ultrasound Imaging

Abnormal changes of the microvasculature are reported to be key evidence of the development of several critical diseases, including cancer, progressive kidney disease, and atherosclerotic plaque. Super-resolution ultrasound imaging is an emerging technology that can identify the microvasculature noninvasively, with unprecedented spatial resolution beyond the acoustic diffraction limit. Therefore, it is a promising approach for diagnosing and monitoring the development of diseases. In this review, we introduce current super-resolution ultrasound imaging approaches and their preclinical applications on different animals and disease models. Future directions and challenges to overcome for clinical translations are also discussed.

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.

Functional Ultrasound Imaging: A New Imaging Modality for Neuroscience

Ultrasound sensitivity to slow blood flow motion gained two orders of magnitude in the last decade thanks to the advent of ultrafast ultrasound imaging at thousands of frames per second. In neuroscience, this access to small cerebral vessels flow led to the introduction of ultrasound as a new and full-fledged neuroimaging modality. Much as functional MRI or functional optical imaging, functional Ultrasound (fUS) takes benefit of the neurovascular coupling. Its ease of use, portability, spatial and temporal resolution makes it an attractive tool for functional imaging of brain activity in preclinical imaging. A large and fast-growing number of studies in a wide variety of small to large animal models have demonstrated its potential for neuroscience research. Beyond preclinical imaging, first proof of concept applications in humans are promising and proved a clear clinical interest in particular in human neonates, per-operative surgery, or even for the development of non-invasive brain machine interfaces.

The Advent of Biomolecular Ultrasound Imaging

Ultrasound imaging is one of the most widely used modalities in clinical practice, revealing human prenatal development but also arterial function in the adult brain. Ultrasound waves travel deep within soft biological tissues and provide information about the motion and mechanical properties of internal organs. A drawback of ultrasound imaging is its limited ability to detect molecular targets due to a lack of cell-type specific acoustic contrast. To date, this limitation has been addressed by targeting synthetic ultrasound contrast agents to molecular targets. This molecular ultrasound imaging approach has proved to be successful but is restricted to the vascular space. Here, we introduce the nascent field of biomolecular ultrasound imaging, a molecular imaging approach that relies on genetically encoded acoustic biomolecules to interface ultrasound waves with cellular processes. We review ultrasound imaging applications bridging wave physics and chemical engineering with potential for deep brain imaging.

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.

Fast 3-D Velocity Estimation in 4-D Using a 62 + 62 Row-Column Addressed Array

This article presents an imaging scheme capable of estimating the full 3-D velocity vector field in a volume using row-column addressed arrays (RCAs) at a high volume rate. A 62 + 62 RCA array is employed with an interleaved synthetic aperture sequence. It contains repeated emissions with rows and columns interleaved with B-mode emissions. The sequence contains 80 emissions in total and can provide continuous volumetric data at a volume rate above 125 Hz. A transverse oscillation cross correlation estimator determines all three velocity components. The approach is investigated using Field II simulations and measurements using a specially built 3-MHz 62 + 62 RCA array connected to the SARUS experimental scanner. Both the B-mode and flow sequences have a penetration depth of 14 cm when measured on a tissue-mimicking phantom (0.5-dB/[ [Formula: see text]] attenuation). Simulations of a parabolic flow in a 12-mm-diameter vessel at a depth of 30 mm, beam-to-flow angle of 90°, and xy-rotation of 45° gave a standard deviation (SD) of (3.3, 3.4, 0.4)% and bias of (-3.3, -3.9, -0.1)%, for ( v_x , v_y , and v_z ). Decreasing the beam-to-flow angle to 60° gave an SD of (8.9, 9.1, 0.8)% and bias of (-7.6, -9.5, -7.2)%, showing a slight increase. Measurements were carried out using a similar setup, and pulsing at 2 kHz yielded comparable results at 90° with an SD of (5.8, 5.5, 1.1)% and bias of (1.4, -6.4, 2.4)%. At 60°, the SD was (5.2, 4.7 1.2)% and bias (-4.6, 6.9, -7.4)%. Results from measurements across all tested settings showed a maximum SD of 6.8% and a maximum bias of 15.8% for a peak velocity of 10 cm/s. A tissue-mimicking phantom with a straight vessel was used to introduce clutter, tissue motion, and pulsating flow. The pulsating velocity magnitude was estimated across ten pulse periods and yielded an SD of 10.9%. The method was capable of estimating transverse flow components precisely but underestimated the flow with small beam-to-flow angles. The sequence provided continuous data in both time and space throughout the volume, allowing for retrospective analysis of the flow. Moreover, B-mode planes can be selected retrospectively anywhere in the volume. This shows that tensor velocity imaging (full 3-D volumetric vector flow imaging) can be estimated in 4-D ( x, y, z, and t ) using only 62 channels in receive, making 4-D volumetric imaging implementable on current scanner hardware.

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.

A preliminary study of post-progressive nail-art effects on in vivo nail plate using optical coherence tomography-based intensity profiling assessment

Nail beautification is a widely applied gender independent practice. Excessive nail beautifications and nail-arts have a direct impact on the nail structure and can cause nail disorders. Therefore, the assessment of post-progressive nail-art effects on the nail is essential to maintain optimal nail health and to avoid any undesirable disorders. In this study, in vivo nails were examined in control stage, with a nail-art stage, and after removing the nail-art stage using a 1310 nm spectral-domain optical coherence tomography (SD-OCT) system. The acquired cross-sectional OCT images were analyzed by a laboratory customized signal processing algorithm to obtain scattered intensity profiling assessments that could reveal the effects of nail beautification on the nail plate. The formation and progression of cracks on the nail plate surface were detected as an effect of nail beautification after 72 h of nail-art removal. Changes in backscattered light intensity and nail plate thickness of control and art-removed nails were quantitatively compared. The results revealed the potential feasibility of the developed OCT-based inspection procedure to diagnose post-progressive nail-art effects on in vivo nail plate, which can be helpful to prevent nail plate damages during art removal through real-time monitoring of the boundary between the nail plate and nail-art. Besides nail-art effects, the developed method can also be used for the investigation of nail plate abnormalities by examining the inconsistency of internal and external nail plate structure, which can be diagnosed with both qualitative and quantitative assessments from a clinical perspective.

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks

Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-density scatterers, some of which are closer than the resolution limit of delay-and-sum (DAS) beamforming. A CNN was designed to take radio frequency channel data and return non-overlapping Gaussian confidence maps. The scatterer positions were estimated from the confidence maps by identifying local maxima. On simulated test sets, the CNN method with three plane waves achieved a precision of 1.00 and a recall of 0.91. Localization uncertainties after excluding outliers were ±46 [Formula: see text] (outlier ratio: 4%) laterally and ±26 [Formula: see text] (outlier ratio: 1%) axially. To evaluate the proposed method on measured data, two phantoms containing cavities were 3-D printed and imaged. For the phantom study, the training data were modified according to the physical properties of the phantoms and a new CNN was trained. On an uniformly spaced scatterer phantom, a precision of 0.98 and a recall of 1.00 were achieved with the localization uncertainties of ±101 [Formula: see text] (outlier ratio: 1%) laterally and ±37 [Formula: see text] (outlier ratio: 1%) axially. On a randomly spaced scatterer phantom, a precision of 0.59 and a recall of 0.63 were achieved. The localization uncertainties were ±132 [Formula: see text] (outlier ratio: 0%) laterally and ±44 [Formula: see text] with a bias of 22 [Formula: see text] (outlier ratio: 0%) axially. This method can potentially be extended to detect highly concentrated microbubbles in order to shorten data acquisition times of super-resolution ultrasound imaging.

Elastic Deformation of Soft Tissue-Mimicking Materials Using a Single Microbubble and Acoustic Radiation Force

Mechanical effects of microbubbles on tissues are central to many emerging ultrasound applications. Here, we investigated the acoustic radiation force a microbubble exerts on tissue at clinically relevant therapeutic ultrasound parameters. Individual microbubbles administered into a wall-less hydrogel channel (diameter: 25-100 µm, Young's modulus: 2-8.7 kPa) were exposed to an acoustic pulse (centre frequency: 1 MHz, pulse length: 10 ms, peak-rarefactional pressures: 0.6-1.0 MPa). Using high-speed microscopy, each microbubble was tracked as it pushed against the hydrogel wall. We found that a single microbubble can transiently deform a soft tissue-mimicking material by several micrometres, producing tissue loading-unloading curves that were similar to those produced using other indentation-based methods. Indentation depths were linked to gel stiffness. Using a mathematical model fitted to the deformation curves, we estimated the radiation force on each bubble (typically tens of nanonewtons) and the viscosity of the gels. These results provide insight into the forces exerted on tissues during ultrasound therapy and indicate a potential source of bio-effects.

Impact of Aperture, Depth, and Acoustic Clutter on the Performance of Coherent Multi-Transducer Ultrasound Imaging

Transducers with a larger aperture size are desirable in ultrasound imaging to improve resolution and image quality. A coherent multi-transducer ultrasound imaging system (CoMTUS) enables an extended effective aperture through the coherent combination of multiple transducers. In this study, the discontinuous extended aperture created by CoMTUS and its performance for deep imaging and through layered media are investigated by both simulations and experiments. Typical image quality metrics—resolution, contrast and contrast-to-noise ratio—are evaluated and compared with a standard single probe imaging system. Results suggest that the image performance of CoMTUS depends on the relative spatial location of the arrays. The resulting effective aperture significantly improves resolution, while the separation between the arrays may degrade contrast. For a limited gap in the effective aperture (less than a few centimetres), CoMTUS provides benefits to image quality compared to the standard single probe imaging system. Overall, CoMTUS shows higher sensitivity and reduced loss of resolution with imaging depth. In general, CoMTUS imaging performance was unaffected when imaging through a layered medium with different speed of sound values and resolution improved up to 80% at large imaging depths.

Super-Resolution Imaging with Ultrasound for Visualization of the Renal Microvasculature in Rats Before and After Renal Ischemia: A Pilot Study

In vivo monitoring of the microvasculature is relevant since diseases such as diabetes, ischemia, or cancer cause microvascular impairment. Super-resolution ultrasound imaging allows in vivo examination of the microvasculature by detecting and tracking sparsely distributed intravascular microbubbles over a minute-long period. The ability to create detailed images of the renal vasculature of Sprague-Dawley rats using a modified clinical ultrasound platform was investigated in this study. Additionally, we hypothesized that early ischemic damage to the renal microcirculation could be visualized. After a baseline scan of the exposed kidney, 10 rats underwent clamping of the renal vein (n = 5) or artery (n = 5) for 45 min. The kidneys were rescanned at the onset of clamp release and after 60 min of reperfusion. Using a processing pipeline for tissue motion compensation and microbubble tracking, super-resolution images with a very high level of detail were constructed. Image filtration allowed further characterization of the vasculature by isolating specific vessels such as the ascending vasa recta with a 15–20 μm diameter. Using the super-resolution images alone, it was only possible for six assessors to consistently distinguish the healthy renal microvasculature from the microvasculature at the onset of vein clamp release. Future studies will aim at attaining quantitative estimations of alterations in the renal microvascular blood flow using super-resolution ultrasound imaging.

Enhanced axillary assessment using intradermally injected microbubbles and contrast-enhanced ultrasound (CEUS) before neoadjuvant systemic therapy (NACT) identifies axillary disease missed by conventional B-mode ultrasound that may be clinically relevant

PURPOSE

The purpose of this study is to measure pre-treatment diagnostic yield of malignant lymph nodes (LN) using contrast-enhanced ultrasound (CEUS) in addition to B-mode axillary ultrasound and compare clinicopathological features, response to NACT and long-term outcomes of patients with malignant LN detected with B-mode ultrasound versus CEUS.

METHODS

Between August 2009 and October 2016, NACT patients were identified from a prospective database. Follow-up data were collected until May 2019.

RESULTS

288 consecutive NACT patients were identified; 77 were excluded, 110 had malignant LN identified by B-mode ultrasound (Group A) and 101 patients with negative B-mode axillary ultrasound had CEUS with biopsy of sentinel lymph nodes (SLN). In two cases CEUS failed. Malignant SLN were identified in 35/99 (35%) of B-mode ultrasound-negative cases (Group B). Patients in Group A were similar to those in Group B in age, mean diagnostic tumour size, grade and oestrogen receptor status. More Group A patients had a ductal phenotype. In the breast, 34 (31%) Group A patients and 8 (23%) Group B patients achieved a pathological complete response (PCR). In the axilla, 41 (37%) and 13 (37%) Groups A and B patients, respectively, had LN PCR. The systemic relapse rate was not statistically different (5% and 16% for Groups A and B, respectively).

CONCLUSIONS

Enhanced assessment with CEUS before NACT identifies patients with axillary metastases missed by conventional B-mode ultrasound. Without CEUS, 22 (63%) of cases in Group B (negative B-mode ultrasound) may have been erroneously classed as progressive disease by surgical SLN excision after NACT.

Deep Learning for Ultrasound Localization Microscopy

By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the spatial resolution of ultrasound (US) imaging and will be helpful for clinical diagnosis. Nevertheless, several challenges remain in fast ULM imaging. The main problems are that current localization methods used to implement fast ULM imaging, e.g., a previously reported localization method based on sparse recovery (CS-ULM), suffer from long data-processing time and exhaustive parameter tuning (optimization). To address these problems, in this paper, we propose a ULM method based on deep learning, which is achieved by using a modified sub-pixel convolutional neural network (CNN), termed as mSPCN-ULM. Simulations and in vivo experiments are performed to evaluate the performance of mSPCN-ULM. Simulation results show that even if under high-density condition (6.4 MBs/mm²), a high localization precision ( [Formula: see text] in the lateral direction and [Formula: see text] in the axial direction) and a high localization reliability (Jaccard index of 0.66) can be obtained by mSPCN-ULM, compared to CS-ULM. The in vivo experimental results indicate that with plane wave scan at a transmit center frequency of 15.625 MHz, microvessels with diameters of [Formula: see text] can be detected and adjacent microvessels with a distance of [Formula: see text] can be separated. Furthermore, when using GPU acceleration, the data-processing time of mSPCN-ULM can be shortened to ~6 sec/frame in the simulations and ~23 sec/frame in the in vivo experiments, which is 3-4 orders of magnitude faster than CS-ULM. Finally, once the network is trained, mSPCN-ULM does not need parameter tuning to implement ULM. As a result, mSPCN-ULM opens the door to implement ULM with fast data-processing speed, high imaging accuracy, short data-acquisition time, and high flexibility (robustness to parameters) characteristics.

Breaking the resolution limit in photoacoustic imaging using non-negativity and sparsity

The spatial resolution achievable in photoacoustic imaging decreases with the imaging depth, resulting in blurred images for deeper structures. Apart from technical limitations, the ultimate resolution limit results from the second law of thermodynamics. The attenuation of the optically generated acoustic waves on their way from the imaged structure to the sample surface by scattering and dissipation leads to an increase of entropy. The resulting loss of spatial resolution for structures embedded in attenuating media can be compensated by numerical methods that make use of additional available information. In this article, we demonstrate this using experimental data from plane one-dimensional (1D) acoustic waves propagating in fat tissue. The acoustic waves are optically induced by nanosecond laser pulses and measured with piezoelectric transducers. The experimental results of 1D compensation are also relevant for photoacoustic imaging in 2D or 3D in an acoustically attenuating medium by dividing the reconstruction problem into two steps: First, the ideal signal, which is the solution of the un-attenuated wave equation, is determined by the proposed 1D attenuation compensation for each detector signal. In a second step, any ultrasound reconstruction method for un-attenuated data can be used for image reconstruction. For the reconstruction of a small step milled into a silicon wafer surface, which allows the generation of two photoacoustic pulses with a small time offset, we take advantage of non-negativity and sparsity and inverted the measured, frequency dependent acoustic attenuation of the fat tissue. We were able to improve the spatial resolution for imaging through 20 mm of porcine fat tissue compared to the diffraction limit at the cut-off frequency by at least a factor of two.

In Vivo Confocal Imaging of Fluorescently Labeled Microbubbles: Implications for Ultrasound Localization Microscopy

We report the time kinetics of fluorescently labeled microbubbles (MBs) in capillary-level microvasculature as measured via confocal microscopy and compare these results to ultrasound localization microscopy (ULM). The observed 19.4 ± 4.2 MBs per confocal field-of-view ( [Formula: see text]) are in excellent agreement with the expected count of 19.1 MBs per frame. The estimated time to fully perfuse this capillary network was 193 s, which corroborates the values reported in the literature. We then modeled the capillary network as an empirically determined discrete-time Markov chain with adjustable MB transition probabilities though individual capillaries. The Monte Carlo random walk simulations found perfusion times ranging from 24.5 s for unbiased Markov chains up to 182 s for heterogeneous flow distributions. This pilot study confirms a probability-derived explanation for the long acquisition times required for super-resolution ULM.

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging

Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in SR-US, such as singular value decomposition (SVD), are computationally burdensome and performed offline. Deep learning has been applied to many biomedical imaging problems, and trained neural networks have been shown to process an image in milliseconds. The goal of this study was to evaluate the effectiveness of deep learning to realize a spatiotemporal filter in the context of SR-US processing. A 3-D convolutional neural network (3DCNN) was trained on in vitro and in vivo data sets using SVD as ground truth in tissue clutter reduction. In vitro data were obtained from a tissue-mimicking flow phantom, and in vivo data were collected from murine tumors of breast cancer. Three training techniques were studied: training with in vitro data sets, training with in vivo data sets, and transfer learning with initial training on in vitro data sets followed by fine-tuning with in vivo data sets. The neural network trained with in vitro data sets followed by fine-tuning with in vivo data sets had the highest accuracy at 88.0%. The SR-US images produced with deep learning allowed visualization of vessels as small as [Formula: see text] in diameter, which is below the diffraction limit (wavelength of [Formula: see text] at 14 MHz). The performance of the 3DCNN was encouraging for real-time SR-US imaging with an average processing frame rate for in vivo data of 51 Hz with GPU acceleration.

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.

Photoacoustic reconstruction from photothermal measurements including prior information

Photothermal measurements with an infrared camera enable a fast and contactless part inspection. The main drawback of existing reconstruction methods is the degradation of the spatial resolution with increasing imaging depth, which results in blurred images for deeper lying structures. In this paper, we propose an efficient image reconstruction strategy that allows prior information to be included to overcome the diffusion-based information loss. Following the virtual wave concept, in a first step we reconstruct an acoustic wave field that satisfies the standard wave equation. Therefore, in the second step, stable and efficient reconstruction methods developed for photoacoustic tomography can be used. We compensate for the loss of information in thermal measurements by incorporating the prior information positivity and sparsity. Therefore, we combine circular projections with an iterative regularization scheme. Using simulated and experimental data, this work demonstrates that the quality of the reconstruction from photothermal measurements can be significantly enhanced.

PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network

In the cytopathology screening of cervical cancer, high-resolution digital cytopathological slides are critical for the interpretation of lesion cells. However, the acquisition of high-resolution digital slides requires high-end imaging equipment and long scanning time. In the study, we propose a GAN-based progressive multi-supervised super-resolution model called PathSRGAN (pathology super-resolution GAN) to learn the mapping of real low-resolution and high-resolution cytopathological images. With respect to the characteristics of cytopathological images, we design a new two-stage generator architecture with two supervision terms. The generator of the first stage corresponds to a densely-connected U-Net and achieves 4× to 10× super resolution. The generator of the second stage corresponds to a residual-in-residual DenseBlock and achieves 10× to 20× super resolution. The designed generator alleviates the difficulty in learning the mapping from 4× images to 20× images caused by the great numerical aperture difference and generates high quality high-resolution images. We conduct a series of comparison experiments and demonstrate the superiority of PathSRGAN to mainstream CNN-based and GAN-based super-resolution methods in cytopathological images. Simultaneously, the reconstructed high-resolution images by PathSRGAN improve the accuracy of computer-aided diagnosis tasks effectively. It is anticipated that the study will help increase the penetration rate of cytopathology screening in remote and impoverished areas that lack high-end imaging equipment.

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.

Validation of Ultrasound Super-Resolution Imaging of Vasa Vasorum in Rabbit Atherosclerotic Plaques

Acute coronary syndromes and strokes are mainly caused by atherosclerotic plaque (AP) rupture. Abnormal increase of vasa vasorum (VV) is reported as a key evidence of plaque progression and vulnerability. However, due to their tiny size, it is still challenging to noninvasively identify VV near the major vessels. Ultrasound super resolution (USR), a technique that provides high spatial resolution beyond the acoustic diffraction limit, demonstrated an adequate spatial resolution for VV detection in early studies. However, a thorough validation of this technology in the plaque model is particularly needed in order to continue further extended preclinical studies. In this letter, we present an experiment protocol that verifies the USR technology for VV identification with subsequent histology and ex vivo micro-computed tomography ( μ CT). Deconvolution-based USR imaging was applied on two rabbits to identify the VV near the AP in the femoral artery. Histology and ex vivo μ CT imaging were performed on excised femoral tissue to validate the USR technique both pathologically and morphologically. This established validation protocol could facilitate future extended preclinical studies toward the clinical translation of USR imaging for VV identification.

Simultaneous Evalulation of Contrast Pulse Sequences for Super-Resolution Ultrasound Imaging - Preliminary In Vitro and In Vivo Results

Super-resolution ultrasound imaging (SR-US) has enabled a tenfold improvement in resolution of the microvasculature with clinical application in many disease processes such as cancer, diabetes and cardiovascular disease. Plane wave ultrasound (US) platforms in turn are capable of the very high frame rates needed to track microbubble (MB) contrast agents used in SR-US. Both B-mode US imaging and contrast enhanced US imaging (CEUS) have been effectively used in SR-US, with B-mode US having higher signal-to-noise ratio (SNR) and CEUS providing higher contrast-to-tissue ratio (CTR). Lengthy imaging time needed for SR-US to allow perfusion and MB detection is an impediment to clinical adoption. Both SNR and CTR improvements can enhance SR-US imaging by enhancing the detection of MBs thus reducing imaging time. This study simultaneously evaluated nonlinear contrast pulse sequences (CPS) employing different amplitude modulation (AM) and pulse inversion (PI) nonlinear CEUS imaging techniques as well as combinations of the two, (AMPI) with B-mode US imaging. The objective was to improve the detection rate of MB during SR-US. Imaging was performed in vitro and in vivo in the rat hind limb using a Vantage 256 research scanner (Verasonics Inc.). Comparisons of four CPS compositions with B-mode US imaging was made based on the number of MB detected and localized in SR-US images. The use of a PI nonlinear CEUS imaging strategy improved SR-US imaging by increasing the number of MB detected in a sequence of frames by an average of 28.3% and up to 52.6% over a B-mode US imaging strategy, which would decrease imaging time accordingly.

Contrast-Enhanced Ultrasound for Musculoskeletal Applications: A World Federation for Ultrasound in Medicine and Biology Position Paper

This World Federation for Ultrasound in Medicine and Biology position paper reviews the diagnostic potential of ultrasound contrast agents for clinical decision-making and provides general advice for optimal contrast-enhanced ultrasound performance in musculoskeletal issues. In this domain, contrast-enhanced ultrasound performance has increasingly been investigated with promising results, but still lacks everyday clinical application and standardized techniques; therefore, experts summarized current knowledge according to published evidence and best personal experience. The goal was to intensify and standardize the use and administration of ultrasound contrast agents to facilitate correct diagnoses and ultimately to improve the management and outcomes of patients.

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

Superharmonic Ultrasound for Motion-Independent Localization Microscopy: Applications to Microvascular Imaging From Low to High Flow Rates

Recent advances in high frame rate biomedical ultrasound have led to the development of ultrasound localization microscopy (ULM), a method of imaging microbubble (MB) contrast agents beyond the diffraction limit of conventional coherent imaging techniques. By localizing and tracking the positions of thousands of individual MBs, ultrahigh resolution vascular maps are generated which can be further analyzed to study disease. Isolating bubble echoes from tissue signal is a key requirement for super-resolution imaging which relies on the spatiotemporal separability and localization of the bubble signals. To date, this has been accomplished either during acquisition using contrast imaging sequences or post-beamforming by applying a spatiotemporal filter to the B-mode images. Superharmonic imaging (SHI) is another contrast imaging method that separates bubbles from tissue based on their strongly nonlinear acoustic properties. This approach is highly sensitive, and, unlike spatiotemporal filters, it does not require decorrelation of contrast agent signals. Since this superharmonic method does not rely on bubble velocity, it can detect completely stationary and moving bubbles alike. In this work, we apply SHI to ULM and demonstrate an average improvement in SNR of 10.3-dB in vitro when compared with the standard singular value decomposition filter approach and an increase in SNR at low flow ( [Formula: see text]/frame) from 5 to 16.5 dB. Additionally, we apply this method to imaging a rodent kidney in vivo and measure vessels as small as [Formula: see text] in diameter after motion correction.

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.

Deep Unfolded Robust PCA With Application to Clutter Suppression in Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and with the fast iterative shrinkage algorithm. We show that our architecture exhibits better image quality and contrast.

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.

Low Rank and Sparse Decomposition of Ultrasound Color Flow Images for Suppressing Clutter in Real-Time

In this work, a novel technique for real-time clutter rejection in ultrasound Color Flow Imaging (CFI) is proposed. Suppressing undesired clutter signal is important because clutter prohibits an unambiguous view of the vascular network. Although conventional eigen-based filters are potentially efficient in suppressing clutter signal, their performance is highly dependent on proper selection of a clutter to blood boundary which is done manually. Herein, we resolve this limitation by formulating the clutter suppression problem as a foreground-background separation problem to extract the moving blood component. To that end, we adapt the fast Robust Matrix Completion (fRMC) algorithm, and utilize the in-face extended Frank-Wolfe method to minimize the rank of the matrix of ultrasound frames. Our method automates the clutter suppression process, which is critical for clinical use. We name the method RAPID (Robust mAtrix decomPosition for suppressIng clutter in ultrasounD) since the automation step can substantially streamline clutter suppression. The technique is validated with simulation, flow phantom and two sets of in-vivo data. RAPID code as well as most of the data in this paper can be downloaded from RAPID.sonography.ai.

Ultrasound super-resolution imaging provides a noninvasive assessment of renal microvasculature changes during mouse acute kidney injury

Acute kidney injury (AKI) is a risk factor for the development of chronic kidney disease (CKD). One mechanism for this phenomenon is renal microvascular rarefaction and subsequent chronic impairment in perfusion. However, diagnostic tools to monitor the renal microvasculature in a noninvasive and quantitative manner are still lacking. Ultrasound super-resolution imaging is an emerging technology that can identify microvessels with unprecedented resolution. Here, we applied this imaging technique to identify microvessels in the unilateral ischemia-reperfusion injury mouse model of AKI-to-CKD progression in vivo. Kidneys from 21 and 42 day post- ischemia-reperfusion injury, the contralateral uninjured kidneys, and kidneys from sham-operated mice were examined by ultrasound super-resolution and histology. Renal microvessels were successfully identified by this imaging modality with a resolution down to 32 μm. Renal fibrosis was observed in all kidneys with ischemia-reperfusion injury and was associated with a significant reduction in kidney size, cortical thickness, relative blood volume, and microvascular density as assessed by this imaging. Tortuosity of the cortical microvasculature was also significantly increased at 42 days compared to sham. These vessel density measurements correlated significantly with CD31 immunohistochemistry (R²=0.77). Thus, ultrasound super-resolution imaging provides unprecedented resolution and is capable of noninvasive quantification of renal vasculature changes associated with AKI-to-CKD progression in mice. Hence, this technique could be a promising diagnostic tool for monitoring progressive kidney disease.

Imaging Methods for Ultrasound Contrast Agents

Microbubble contrast agents were introduced more than 25 years ago with the objective of enhancing blood echoes and enabling diagnostic ultrasound to image the microcirculation. Cardiology and oncology waited anxiously for the fulfillment of that objective with one clinical application each: myocardial perfusion, tumor perfusion and angiogenesis imaging. What was necessary though at first was the scientific understanding of microbubble behavior in vivo and the development of imaging technology to deliver the original objective. And indeed, for more than 25 years bubble science and imaging technology have evolved methodically to deliver contrast-enhanced ultrasound. Realization of the basic bubbles properties, non-linear response and ultrasound-induced destruction, has led to a plethora of methods; algorithms and techniques for contrast-enhanced ultrasound (CEUS) and imaging modes such as harmonic imaging, harmonic power Doppler, pulse inversion, amplitude modulation, maximum intensity projection and many others were invented, developed and validated. Today, CEUS is used everywhere in the world with clinical indications both in cardiology and in radiology, and it continues to mature and evolve and has become a basic clinical tool that transforms diagnostic ultrasound into a functional imaging modality. In this review article, we present and explain in detail bubble imaging methods and associated artifacts, perfusion quantification approaches, and implementation considerations and regulatory aspects.

Three-Dimensional Super-Resolution Imaging Using a Row-Column Array

A 3-D super-resolution (SR) pipeline based on data from a row-column (RC) array is presented. The 3-MHz RC array contains 62 rows and 62 columns with a half wavelength pitch. A synthetic aperture (SA) pulse inversion sequence with 32 positive and 32 negative row emissions is used for acquiring volumetric data using the SARUS research ultrasound scanner. Data received on the 62 columns are beamformed on a GPU for a maximum volume rate of 156 Hz when the pulse repetition frequency is 10 kHz. Simulated and 3-D printed point and flow microphantoms are used for investigating the approach. The flow microphantom contains a 100- [Formula: see text] radius tube injected with the contrast agent SonoVue. The 3-D processing pipeline uses the volumetric envelope data to find the bubble's positions from their interpolated maximum signal and yields a high resolution in all three coordinates. For the point microphantom, the standard deviation on the position is (20.7, 19.8, 9.1) [Formula: see text]. The precision estimated for the flow phantom is below [Formula: see text] in all three coordinates, making it possible to locate structures on the order of a capillary in all three dimensions. The RC imaging sequence's point spread function has a size of 0.58 × 1.05 × 0.31 mm³ ( 1.17λ×2.12λ×0.63λ ), so the possible volume resolution is 28900 times smaller than for SA RC B-mode imaging.

Microvascular Ultrasonic Imaging of Angiogenesis Identifies Tumors in a Murine Spontaneous Breast Cancer Model

The purpose of this study is to determine if microvascular tortuosity can be used as an imaging biomarker for the presence of tumor-associated angiogenesis and if imaging this biomarker can be used as a specific and sensitive method of locating solid tumors. Acoustic angiography, an ultrasound-based microvascular imaging technology, was used to visualize angiogenesis development of a spontaneous mouse model of breast cancer (n = 48). A reader study was used to assess visual discrimination between image types, and quantitative methods utilized metrics of tortuosity and spatial clustering for tumor detection. The reader study resulted in an area under the curve of 0.8, while the clustering approach resulted in the best classification with an area under the curve of 0.95. Both the qualitative and quantitative methods produced a correlation between sensitivity and tumor diameter. Imaging of vascular geometry with acoustic angiography provides a robust method for discriminating between tumor and healthy tissue in a mouse model of breast cancer. Multiple methods of analysis have been presented for a wide range of tumor sizes. Application of these techniques to clinical imaging could improve breast cancer diagnosis, as well as improve specificity in assessing cancer in other tissues. The clustering approach may be beneficial for other types of morphological analysis beyond vascular ultrasound images.

Ultrasound localization microscopy of renal tumor xenografts in chicken embryo is correlated to hypoxia

Ultrasound localization microscopy (ULM) permits the reconstruction of super-resolved microvascular images at clinically relevant penetration depths, which can be potentially leveraged to provide non-invasive quantitative measures of tissue hemodynamics and hypoxic status. We demonstrate that ULM microbubble data processing methods, applied to images acquired with a Verasonics Vantage 256 system, can provide a non-invasive imaging surrogate biomarker of tissue oxygenation status. This technique was applied to evaluate the microvascular structure, vascular perfusion, and hypoxia of a renal cell carcinoma xenograft model grown in the chorioallantoic membrane of chicken embryos. Histological microvascular density was significantly correlated to ULM measures of intervessel distance (R = -0.92, CI95 = [-0.99,-0.42], p = 0.01). The Distance Metric, a measure of vascular tortuosity, was found to be significantly correlated to hypoxyprobe quantifications (R = 0.86, CI95 = [0.17, 0.99], p = 0.03). ULM, by providing non-invasive in vivo microvascular structural information, has the potential to be a crucial clinical imaging modality for the diagnosis and therapy monitoring of solid tumors.