Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors

Quantitative assessment of ultrasound microvessel imaging in Crohn's disease: correlation with pathological inflammation

OBJECTIVE

Ultrasound microvessel imaging (UMI) may offer noninvasive, highly sensitive microvessel imaging for assessing Crohn's disease (CD). However, a quantification metric that demonstrates a strong correlation with pathological inflammation is preferred. The objective was to determine if UMI can enhance IBD imaging interrogations.

METHODS

UMI was performed on bowel wall segments from patients with CD requiring surgery (n = 55 patients). The vessel-length ratio (VLR) measured by UMI was compared with that obtained using color flow imaging (CFI) and with a histopathologic standard evaluated on all bowel layers. Correlations between VLR and pathological inflammation and receiver operating characteristic (ROC) curves between different groups were analyzed to demonstrate the advantages of VLR with UMI.

RESULTS

The correlation between VLR from UMI and pathological inflammation (R = 0.80) outperformed that of VLR from CFI (R = 0.59). UMI showed a significant difference (p < 0.01) between mild and non-mild inflammation cases, while CFI could not (p = 0.014). In the ROC analysis, VLR with UMI demonstrated an area under the curve (AUC) of 0.93, compared to the AUC of 0.80 for VLR with CFI, indicating better identification of pathological inflammation between mild and non-mild cases. For a sub-cohort of patients with stricture without penetrating complications (n = 19), VLR using UMI also showed better correlation (R = 0.93) with pathological inflammation scores and a higher AUC (0.96) than those of VLR using CFI (R = 0.66 and 0.88, respectively).

CONCLUSIONS

UMI enhances vessel detection sensitivity compared to CFI and more accurately reflects transmural inflammation in small bowel Crohn's disease. VLR using UMI strongly correlates with pathological inflammation, distinguishing between mild and non-mild cases, notably including patients with stricture without penetrating complications.

KEY POINTS

Question Bowel wall thickness and Limberg score from ultrasound are insufficient quantitative metrics for reliable diagnosis of inflammation severity for Crohn's disease. Findings Ultrasound microvessel imaging (UMI) with vessel-length ratio (VLR) is strongly correlated with pathological inflammation and had improved distinction between mild and non-mild inflammation cases. Clinical relevance UMI with VLR has the potential to enhance clinicians' ability to assess disease activity and evaluate therapeutic responses, thereby improving Crohn's disease patient outcomes.

Precision of in vivo pressure gradient estimations using synthetic aperture ultrasound

Non-invasive estimation of pressure differences using 2D synthetic aperture ultrasound imaging offers a precise, low-cost, and risk-free diagnostic tool. Unlike invasive techniques, this preserves natural blood flow and avoids the limitations of devices that occupy lumen space. This paper evaluates a previously published estimator, modified to incorporate Singular Value Decomposition (SVD) echo-cancellation, using data from ten healthy volunteers and one patient. It is hypothesized that the estimator will enable precise pressure differences from the common carotid artery with a coefficient of variation of approximately 10% over a 10-second data acquisition period. Here, precision is essential to demonstrate the method's consistency and its ability to differentiate between healthy and diseased arteries at the earliest possible stage. Data are acquired using a GE-9L-D, 5.2 MHz linear transducer connected to a Vantage 256 research scanner. The estimator was applied to the left common carotid artery of ten healthy volunteers, with precision being evaluated over the recorded heart cycles by using the coefficient of variation. Eight out of ten individuals showed precision below 10%, whereas two individuals showed precision above 20%. The best precision was attained by subject_03 with a coefficient of variation of 4.64% (16.1 Pa) and the worst precision was attained by subject 09 with a coefficient of variation of 23.3% (30.2 Pa). The average range of pressure differences across volunteers (from maximum positive to maximum negative pressure difference) was 297 Pa when measured across a 14 mm streamline. The corresponding average coefficient of variation was found to be 9.95% (24.6 Pa). A comparison of peak systolic velocities between the experimental scanner and the reference scanner demonstrates a strong positive linear correlation (R2 = 0.76). The corresponding slope of the linear best fit is 0.95, indicating that the relationship between the two scanners is close to a one-to-one match, with the experimental scanner's measurements being slightly less than those of the reference scanner. Finally, data attained from a single patient example shows pressure differences ranging from -61.81 Pa to 1240.82 Pa with blood velocities as high as 1.73 m/s, which is significantly higher than seen in any of the healthy volunteers, supporting the likelihood of differentiating between stenosis grades in future studies. While this study is limited to 10 healthy volunteers and one patient, a different study design is needed to quantify the severity of stenosis and correlate it with pressure differences.

Dual-frequency excitation in high-frame-rate ultrasonic backscatter coefficient analysis of hemorheological properties

Hemorheological properties, such as erythrocyte aggregation can be assessed by ultrasonic backscatter coefficient analysis. In this study, a data-acquisition sequence with dual-frequency (dual-f) excitation was proposed to expand the ultrasonic frequency bandwidth with high-frame-rate imaging. The approach was experimentally validated using ex vivo porcine blood measurements and in vivo human imaging. The center frequency of the excitation wave was alternated between 7.8 (f1) and 12.5 (f2) MHz in the frequency spectral analysis using the reference phantom method. The frequency spectra revealed that the dual-f sequence achieved a bandwidth of 4.5-15 MHz at -20 dB, almost equivalent to those achieved with conventional single-frequency excitation (5.0-15 MHz) with a short-duration wave at 10 MHz (mono-f) in reference media with the sufficient condition of signal-to-noise ratio. The aggregation and disaggregation states of porcine blood suspended in high-molecular-weight dextran were determined by the isotropic diameter and packing factor using the structure factor size estimator. The discrimination performance of the dual-f approach increased, owing to the broadband frequency responses, in contrast with the limited performance of mono-f due to a low signal-to-noise ratio. This approach incorporating dual-f sequence is beneficial for obtaining robustly frequency spectra of hemorheological properties from in vivo scenarios.

Towards quantitative assessment of cerebrovascular autoregulation in human neonates using ultrafast ultrasound imaging

Newborns with congenital heart diseases requiring cardiopulmonary bypass (CPB) are at risk of neurodevelopmental impairment. The impact of deep hypothermia cardiopulmonary bypass (DH-CPB) on cerebrovascular autoregulation (CAR) that controls brain perfusion in the presence of blood pressure variation is not well understood. Recently, ultrafast power Doppler (UPD) showed potential to study CAR in neonates based on cerebral blood volume (CBV). However, since CAR relies mainly on arterial vasoconstriction/vasodilation, monitoring of brain perfusion variation based on CBV requires the discrimination of arterial from venous CBV. This study aims to use UPD combined with an algorithm for the discrimination of arteries and veins to monitor CAR during DH-CPB in neonates. Transfontanellar ultrafast power Doppler was performed in two groups of newborns: those undergoing deep hypothermic cardiopulmonary bypass with circulatory arrest (18-20 °C, n = 6, "DH group") and those undergoing full-flow CPB at mild hypothermia (32-34 °C, n = 6, "non-DH group"). Blood flow directionality was used to differentiate arterial compartments of CBV from venous CBV in specific brain regions where arterial and venous flows exhibit opposite directions. To study CAR, a linear mixed effect model was used to find the association between arterial CBV and mean arterial blood pressure (MAP). In the "non-DH group", we found a negative association between arterial CBV and MAP, indicating that an increase in MAP is associated with a decrease in arterial CBV (slope = -0.020 [Formula: see text], p = 0.047). Conversely, in the "DH group" no significant association was found such that arterial CBV remained stable as MAP increased (p = 0.314). We interpret the reduction in arterial CBV with increasing MAP in the "non-DH group" as an active arterial vasoconstriction triggered by CAR, whereas the lack of variation of arterial CBV in the DH group suggests impaired CAR response. Our findings highlight the potential of ultrafast ultrasound imaging for intra-operative CAR monitoring, paving the way for a better understanding of the impact of different types of CPB on cerebral perfusion.

Transcranial Functional Ultrasound Imaging Detects Focused Ultrasound Neuromodulation Induced Hemodynamic Changes In Vivo

Background

Focused ultrasound (FUS) is an emerging non-invasive technique for neuromodulation in the central nervous system (CNS). Functional ultrasound imaging (fUSI) leverages ultrafast Power Doppler Imaging (PDI) to detect changes in cerebral blood volume (CBV), which correlate well with neuronal activity and thus hold promise to monitor brain responses to FUS.

Objective

Investigate the immediate and short-term effects of transcranial FUS neuromodulation in the brain with fUSI by characterizing hemodynamic responses.

Methods

We designed a setup that aligns a FUS transducer with a linear array to allow immediate subsequent monitoring of the hemodynamic response with fUSI during and after FUS neuromodulation (FUS-fUSI) in lightly anesthetized mice. We investigated the effects of varying pressures and transducer positions on the hemodynamic responses.

Results

We found that higher FUS pressures increase the size of the activated brain area, as well as the magnitude of change in CBV and could show that sham sonications did not produce hemodynamic responses. Unilateral sonications resulted in bilateral hemodynamic changes with a significantly stronger response on the ipsilateral side. FUS neuromodulation in mice with a cranial window showed distinct activation patterns that were frequency-dependent and different from the activation patterns observed in the transcranial model.

Conclusion

fUSI is hereby shown capable of transcranially monitoring online and short-term hemodynamic effects in the brain during and following FUS neuromodulation.

Monitoring microvascular changes over time with a repositionable 3D ultrasonic capacitive micromachined row-column sensor

eHealth devices, including smartwatches and smart scales, have the potential to transform health care by enabling continuous, real-time monitoring of vital signs over extended periods. Existing technologies, however, lack comprehensive monitoring of the microvascular network, which is linked to conditions such as diabetes, hypertension, and small vessel diseases. This study introduces an ultrasound approach using a capacitive micromachined ultrasound transducer row-column array for continuous, ultrasensitive three-dimensional (3D) Doppler imaging of microvascular changes such as hemodynamic variations or vascular remodeling. In vitro tests and in vivo studies with healthy volunteers demonstrated the sensor's ability to image the 3D microvascular network at high resolution over different timescales with automatic registration and to detect microvascular changes with high sensitivity. Integrating this technology into wearable devices could, one day, enhance understanding, monitoring, and possibly early detection of microvascular-related health conditions.

Computationally Efficient SVD Filtering for Ultrasound Flow Imaging and Real-Time Application to Ultrafast Doppler

Over the past decade, ultrasound microvasculature imaging has seen the rise of highly sensitive techniques, such as ultrafast power Doppler (UPD) and ultrasound localization microscopy (ULM). The cornerstone of these techniques is the acquisition of a large number of frames based on unfocused wave transmission, enabling the use of singular value decomposition (SVD) as a powerful clutter filter to separate microvessels from surrounding tissue. Unfortunately, SVD is computationally expensive, hampering its use in real-time UPD imaging and weighing down the ULM processing chain, with evident impact in a clinical context. To solve this problem, we propose a new approach to implement SVD filtering, based on simplified and elementary operations that can be optimally parallelized on GPU (GPU sSVD), unlike standard SVD algorithms that are mainly serial. First, we show that GPU sSVD filters UPD and ULM data with high computational efficiency compared to standard SVD implementations, and without losing image quality. Second, we demonstrate that the proposed method is suitable for real-time operation. GPU sSVD was embedded in a research scanner, along with the spatial similarity matrix (SSM), a well-known efficient approach to automate the selection of SVD blood components. High real-time throughput of GPU sSVD is demonstrated when using large packets of frames, with and without SSM. For example, more than 15000 frames/s were filtered with 512 packet size on a 128 × 64 samples beamforming grid. Finally, GPU sSVD was used to perform, for the first time, UPD imaging with real-time and adaptive SVD filtering on healthy volunteers.

Adaptive multilevel thresholding for SVD-based clutter filtering in ultrafast transthoracic coronary flow imaging

BACKGROUND AND OBJECTIVE

The integration of ultrafast Doppler imaging with singular value decomposition clutter filtering has demonstrated notable enhancements in flow measurement and Doppler sensitivity, surpassing conventional Doppler techniques. However, in the context of transthoracic coronary flow imaging, additional challenges arise due to factors such as the utilization of unfocused diverging waves, constraints in spatial and temporal resolution for achieving deep penetration, and rapid tissue motion. These challenges pose difficulties for ultrafast Doppler imaging and singular value decomposition in determining optimal tissue-blood (TB) and blood-noise (BN) thresholds, thereby limiting their ability to deliver high-contrast Doppler images.

METHODS

This study introduces a novel local blood subspace detection method that utilizes multilevel thresholding by the valley-emphasized Otsu's method to estimate the TB and BN thresholds on a pixel-based level, operating under the assumption that the magnitude of the spatial singular vector curve of each pixel resembles the shape of a trimodal Gaussian. Upon obtaining the local TB and BN thresholds, a weighted mask (WM) is generated to assess the blood content in each pixel. To enhance the computational efficiency of this pixel-based algorithm, a dedicated tree-structure k-means clustering approach, further enhanced by noise rejection (NR) at each singular vector order, is proposed to group pixels with similar spatial singular vector curves, subsequently applying local thresholding (LT) on a cluster-based (CB) level.

RESULTS

The effectiveness of the proposed method was evaluated using an ex-vivo setup featuring a Langendorff swine heart. Comparative analysis with power Doppler images filtered using the conventional global thresholding method, which uniformly applies TB and BN thresholds to all pixels, revealed noteworthy enhancements. Specifically, our proposed CBLT+NR+WM approach demonstrated an average 10.8-dB and 11.2-dB increase in Contrast-to-Noise ratio and Contrast in suppressing the tissue signal, paralleled by an average 5-dB (Contrast-to-Noise ratio) and 9-dB (Contrast) increase in suppressing the noise signal.

CONCLUSIONS

These results clearly indicate the capability of our method to attenuate residual tissue and noise signals compared to the global thresholding method, suggesting its promising utility in challenging transthoracic settings for coronary flow measurement.

A Tracking Prior to Localization Workflow for Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) has proven effective in resolving microvascular structures and local mean velocities at sub-diffraction-limited scales, offering high-resolution imaging capabilities. Dynamic ULM (DULM) enables the creation of angiography or velocity movies throughout cardiac cycles. Currently, these techniques rely on a Localization-and-Tracking (LAT) workflow consisting in detecting microbubbles (MB) in the frames before pairing them to generate tracks. While conventional LAT methods perform well at low concentrations, they suffer from longer acquisition times and degraded localization and tracking accuracy at higher concentrations, leading to biased angiogram reconstruction and velocity estimation. In this study, we propose a novel approach to address these challenges by reversing the current workflow. The proposed method, Tracking-and-Localization (TAL), relies on first tracking the MB and then performing localization. Through comprehensive benchmarking using both in silico and in vivo experiments and employing various metrics to quantify ULM angiography and velocity maps, we demonstrate that the TAL method consistently outperforms the reference LAT workflow. Moreover, when applied to DULM, TAL successfully extracts velocity variations along the cardiac cycle with improved repeatability. The findings of this work highlight the effectiveness of the TAL approach in overcoming the limitations of conventional LAT methods, providing enhanced ULM angiography and velocity imaging.

Microvascular blood flow ultrasound imaging with microbubble-based H-Scan technology

Ultrasound blood flow imaging plays a crucial role in the diagnosis of cardiovascular and cerebrovascular diseases. Conventional ultrafast ultrasound plane-wave imaging techniques have limited capabilities in microvascular imaging. To enhance the quality of blood flow imaging, this study proposes a microbubble-based H-Scan ultrasound imaging technique. This technique utilizes high-order H-Scan to detect the Rayleigh scattering contributed by blood flow and microbubbles at certain concentrations. The detected results are then processed in the B channel using methods such as clutter filtering based on Casorati matrix singular value decomposition (Casorati-SVD). Compared with the control group without H-Scan, the signal-to-noise ratio (SNR) of the experimental group using the microbubble-based H-Scan ultrasound imaging technique was increased by 38.61% on average and the contrast signal-to-noise ratio (CNR) was increased by 39.5% on average. The improved image quality of microvascular flow imaging was visibly enhanced. This method demonstrates significant advantages in enhancing the sensitivity and accuracy of ultrasound blood flow imaging, indicating considerable potential for clinical applications.

3D ultrasound localization microscopy of the nonhuman primate brain

BACKGROUND

Haemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualisation and quantification of cerebral blood flow would help better understand the underlying mechanism of these cerebrovascular diseases.

METHODS

3D ultrasound localization microscopy (ULM) is a recently developed technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. In this study, we tested the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channel programmable ultrasound scanner.

FINDINGS

We achieved a highly resolved vascular map of the macaque brain at large depth (down to 3 cm) in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 μm. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60 μm.

INTERPRETATION

This work paves the way to clinical applications of 3D ULM. In particular, transcranial 3D ULM in humans could become a tool for the non-invasive study and monitoring of the brain cerebrovascular changes occurring in neurological diseases.

FUNDING

This work was supported by the New Frontier in Research Fund (NFRFE-2022-00590), by the Canada Foundation for Innovation under grant 38095, by the Natural Sciences and Engineering Research Council of Canada (NSERC) under discovery grant RGPIN-2020-06786, by Brain Canada under grant PSG2019, and by the Canadian Institutes of Health Research (CIHR) under grant PJT-156047 and MPI-452530. Computing support was provided by the Digital Research Alliance of Canada.

Unveiling the potential of ultrasound in brain imaging: Innovations, challenges, and prospects

Within medical imaging, ultrasound serves as a crucial tool, particularly in the realms of brain imaging and disease diagnosis. It offers superior safety, speed, and wider applicability compared to Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT). Nonetheless, conventional transcranial ultrasound applications in adult brain imaging face challenges stemming from the significant acoustic impedance contrast between the skull bone and soft tissues. Recent strides in ultrasound technology encompass a spectrum of advancements spanning tissue structural imaging, blood flow imaging, functional imaging, and image enhancement techniques. Structural imaging methods include traditional transcranial ultrasound techniques and ultrasound elastography. Transcranial ultrasound assesses the structure and function of the skull and brain, while ultrasound elastography evaluates the elasticity of brain tissue. Blood flow imaging includes traditional transcranial Doppler (TCD), ultrafast Doppler (UfD), contrast-enhanced ultrasound (CEUS), and ultrasound localization microscopy (ULM), which can be used to evaluate the velocity, direction, and perfusion of cerebral blood flow. Functional ultrasound imaging (fUS) detects changes in cerebral blood flow to create images of brain activity. Image enhancement techniques include full waveform inversion (FWI) and phase aberration correction techniques, focusing on more accurate localization and analysis of brain structures, achieving more precise and reliable brain imaging results. These methods have been extensively studied in clinical animal models, neonates, and adults, showing significant potential in brain tissue structural imaging, cerebral hemodynamics monitoring, and brain disease diagnosis. They represent current hotspots and focal points of ultrasound medical research. This review provides a comprehensive summary of recent developments in brain imaging technologies and methods, discussing their advantages, limitations, and future trends, offering insights into their prospects.

Longitudinal Awake Imaging of Mouse Deep Brain Microvasculature with Super-resolution Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves microvasculature in deep tissues with high spatial resolution. However, existing preclinical ULM applications are largely constrained to anesthetized animals, introducing confounding vascular effects such as vasodilation and altered hemodynamics. As such, ULM quantifications (e.g., vessel diameter, density, and flow velocity) may be confounded by the use of anesthesia, undermining the usefulness of ULM in practice. Here we introduce a method to address this limitation and achieve ULM imaging in awake mouse brain. Pupillary monitoring was used to support the presence of the awake state during ULM imaging. Vasodilation induced by isoflurane was observed by ULM. Upon recovery to the awake state, reductions in vessel density and flow velocity were observed across different brain regions. In the cortex, the effects induced by isoflurane are more pronounced on venous flow than on arterial flow. In addition, serial in vivo imaging of the same animal brain at weekly intervals demonstrated the highly robust longitudinal imaging capability of the proposed technique. The consistency was further verified through quantitative analysis on individual vessels, cortical regions of arteries and veins, and subcortical regions. This study demonstrates longitudinal ULM imaging in the awake mouse brain, which is crucial for many ULM brain applications that require awake and behaving animals.

Functional ultrasound and brain connectivity reveal central nervous system compromise in Trembler-J mice model of Charcot-Marie-Tooth disease

The Charcot-Marie-Tooth-1E (CMT1E) disease is typically described as a peripheral neuropathy in humans, causing decreased nerve conduction, spastic paralysis, and tremor. The Trembler-J (TrJ) mice serve as a high fidelity model of this disease. Here, we use functional ultrasound (fUS) and functional connectivity (FC) to analyze TrJ mice's brain activity during sensory stimulation and resting state experiments against wild type (WT) mice - the healthy counterpart. fUS is an imaging technique that measures cerebral blood volume (CBV) temporal changes. We study these changes in the primary somatosensory cortex barrel field (S1BF) of both mice populations during periodic vibrissae stimulation, measuring the number of pixels that correlate to the stimulation (i.e., the size of the activation area), the average correlation of these pixels (i.e., the response strength), and the CBV's rate of change for each stimulation (i.e., the hemodynamic response). Then, we construct a FC matrix for each genotype and experiment by correlating the CBV signals from the eight cortical regions defined by the Paxinos and Franklin atlas. Our results show that TrJ mice have significantly diminished neurovascular responses and altered brain connectivity with respect to WT mice, pointing to central nervous system effects that could shift our understanding of the CMT1E disease.

High-Frame-Rate Ultrasound Velocimetry in the Healthy Femoral Bifurcation: A Comparative Study Against 4-D Flow Magnetic Resonance Imaging

OBJECTIVE

Local flow dynamics impact atherosclerosis yet are difficult to quantify with conventional ultrasound techniques. This study investigates the performance of ultrasound vector flow imaging (US-VFI) with and without ultrasound contrast agents in the healthy femoral bifurcation.

METHODS

High-frame-rate ultrasound data with incremental acoustic outputs were acquired in the femoral bifurcations of 20 healthy subjects before (50V) and after contrast injection (2V, 5V and 10V). 2-D blood-velocity profiles were obtained through native blood speckle tracking (BST) and contrast tracking (echo particle image velocimetry [echoPIV]). As a reference, 4-D flow magnetic resonance imaging (4-D flow MRI) was acquired. Contrast-to-background ratio and vector correlation were used to assess the quality of the US-VFI acquisitions. Spatiotemporal velocity profiles were extracted, from which peak velocities (PSV) were compared between the modalities. Furthermore, root-mean-square error analysis was performed.

RESULTS

US-VFI was successful in 99% of the cases and optimal VFI quality was established with the 10V echoPIV and BST settings. A good correspondence between 10V echoPIV and BST was found, with a mean PSV difference of -0.5 cm/s (limits of agreement: -14.1-13.2). Both US-VFI techniques compared well with 4-D flow MRI, with a mean PSV difference of 1.4 cm/s (-18.7-21.6) between 10V echoPIV and MRI, and 0.3 cm/s (-23.8-24.4) between BST and MRI. Similar complex flow patterns among all modalities were observed.

CONCLUSION

2-D blood-flow quantification of femoral bifurcation is feasible with echoPIV and BST. Both modalities showed good agreement compared to 4-D flow MRI. For the femoral tract the administration of contrast was not needed to increase the echogenicity of the blood for optimal image quality.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

Transcutaneous Imaging of Rabbit Kidney Using 3-D Acoustic Wave Sparsely Activated Localization Microscopy With a Row-Column-Addressed Array

OBJECTIVE

Super-resolution ultrasound (SRUS) imaging through localizing and tracking microbubbles, also known as ultrasound localization microscopy (ULM), can produce sub-diffraction resolution images of micro-vessels. We have recently demonstrated 3-D selective SRUS with a matrix array and phase change contrast agents (PCCAs). However, this method is limited to a small field of view (FOV) and by the complex hardware required.

METHOD

This study proposed 3-D acoustic wave sparsely activated localization microscopy (AWSALM) using PCCAs and a 128+128 row-column-addressed (RCA) array, which offers ultrafast acquisition with over 6 times larger FOV and 4 times reduction in hardware complexity than a 1024-element matrix array. We first validated this method on an in-vitro microflow phantom and subsequently demonstrated non-invasively on a rabbit kidney in-vivo.

RESULTS

Our results show that 3-D AWSALM images of the phantom covering a mm volume can be generated under 5 seconds with an 8 times resolution improvement over the system point spread function. The full volume of the rabbit kidney can be covered to generate 3-D microvascular structure, flow speed and direction super-resolution maps under 15 seconds, combining the large FOV of RCA with the high resolution of SRUS. Additionally, 3-D AWSALM is selective and can visualize the microvasculature within the activation volume and downstream vessels in isolation. Sub-sets of the kidney microvasculature can be imaged through selective activation of PCCAs.

CONCLUSION

Our study demonstrates large FOV 3-D AWSALM using an RCA probe.

SIGNIFICANCE

3-D AWSALM offers an unique in-vivo imaging tool for fast, selective and large FOV vascular flow mapping.

Deep Power-Aware Tunable Weighting for Ultrasound Microvascular Imaging

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave (PW) transmissions at high frame rates. However, low signal-to-noise ratio (SNR) of PWs causes low image quality. Adaptive beamformers have been proposed to suppress noise energy to achieve higher image quality accompanied by increasing computational complexity. Deep learning (DL) leverages powerful hardware capabilities to enable rapid implementation of noise suppression at the cost of flexibility. To enhance the applicability of DL-based methods, in this work, we propose a deep power-aware tunable (DPT) weighting (i.e., postfilter) for delay-and-sum (DAS) beamforming to improve UMI by enhancing PW images. The model, called Yformer, is a hybrid structure combining convolution and Transformer. With the DAS beamformed and compounded envelope image as input, Yformer can estimate both noise power and signal power. Furthermore, we utilize the obtained powers to compute pixel-wise weights by introducing a tunable noise control factor (NCF), which is tailored for improving the quality of different UMI applications. In vivo experiments on the rat brain demonstrate that Yformer can accurately estimate the powers of noise and signal with the structural similarity index measure (SSIM) higher than 0.95. The performance of the DPT weighting is comparable to that of superior adaptive beamformer in uPDI with low computational cost. The DPT weighting was then applied to four different datasets of ULM, including public simulation, public rat brain, private rat brain, and private rat liver datasets, showing excellent generalizability using the model trained by the private rat brain dataset only. In particular, our method indirectly improves the resolution of liver ULM from 25.24 to m by highlighting small vessels. In addition, the DPT weighting exhibits more details of blood vessels with faster processing, which has the potential to facilitate the clinical applications of high-quality UMI.

Multi-Parametric Retinal Microvascular Functional Perfusion Imaging Based on Dynamic Fundus Fluorescence Angiography

Retinal microvascular disease has caused serious visual impairment widely in the world, which can be hopefully prevented via early and precision microvascular hemodynamic diagnosis. Due to artifacts from choroidal microvessels and tiny movements, current fundus microvascular imaging techniques including fundus fluorescein angiography (FFA) precisely identify retinal microvascular microstructural damage and abnormal hemodynamic changes difficulty, especially in the early stage. Therefore, this study proposes an FFA-based multi-parametric retinal microvascular functional perfusion imaging (RM-FPI) scheme to assess the microstructural damage and quantify its hemodynamic distribution precisely. Herein, a spatiotemporal filter based on singular value decomposition combined with a lognormal fitting model was used to remove the above artifacts. Dynamic FFAs of patients (n = 7) were collected first. The retinal time fluorescence intensity curves were extracted and the corresponding perfusion parameters were estimated after decomposition filtering and model fitting. Compared with in vivo results without filtering and fitting, the signal-to-clutter ratio of retinal perfusion curves, average contrast, and resolution of RM-FPI were up to 7.32 ± 0.43 dB, 14.34 ± 0.24 dB, and 11.0 ± 2.0 µm, respectively. RM-FPI imaged retinal microvascular distribution and quantified its spatial hemodynamic changes, which further characterized the parabolic distribution of local blood flow within diameters ranging from 9 to 400 µm. Finally, RM-FPI was used to quantify, visualize, and diagnose the retinal hemodynamics of retinal vein occlusion from mild to severe. Therefore, this study provided a scheme for early and precision diagnosis of retinal microvascular disease, which might be beneficial in preventing its development.

Correlations between ultrafast power Doppler perfusion imaging variables and clinical disease activity in rheumatoid arthritis: potential applications for diagnosing and treating patients in deep clinical remission

PURPOSE

This study aimed to evaluate the ability of ultrafast power Doppler (PD) to assess disease activity in rheumatoid arthritis (RA) by examining the correlations between variables from ultrafast PD perfusion imaging and clinical measures of disease activity.

METHODS

Thirty-three RA patients underwent clinical assessments of disease activity and ultrasound scans of bilateral wrists using both ultrafast and conventional PD systems. A spatial singular value decomposition filter was applied to the ultrafast PD imaging. Singular vectors representing perfusion and fast flows were selected to produce perfusion images. All images were quantitatively analyzed with computer assistance and scored semiquantitatively (0-3) by a physician for synovial vascularity. The Pearson correlation coefficients between image variables and clinical indices were calculated.

RESULTS

The correlation coefficients ranged from weakly to moderately positive between ultrafast PD variables and clinical indices (r=0.221-0.374, all P<0.05). The strongest correlations were observed for synovial PD brightness with the 28-joint Disease Activity Score based on C-Reactive Protein (DAS28-CRP) and the Simplified Disease Activity Index (SDAI). In patients within the deep clinical remission (dCR) subgroup, synovial PD brightness showed stronger correlations with DAS28-CRP, the Clinical Disease Activity Index, and SDAI (r=0.578-0.641, all P<0.001). The correlation coefficients between conventional PD variables and clinical indices were similar to those observed with ultrafast PD variables.

CONCLUSION

Ultrafast PD imaging effectively extracts capillary blood signals and generates perfusion images. In the RA population, ultrafast PD variables exhibit weak-to-moderate correlations with clinical indices, with these correlations being notably stronger in dCR patients.

Super-resolution ultrasound imaging reveals temporal cerebrovascular changes with disease progression in female 5×FAD mouse model of Alzheimer's disease: correlation with pathological impairments

BACKGROUND

Vascular dysfunction is closely associated with the progression of Alzheimer's disease (AD). A critical research gap exists that no studies have explored the in vivo temporal changes of cerebrovascular alterations with AD progression in mouse models, encompassing both structure and flow dynamics at micron-scale resolution across the early, middle, and late stages of the disease.

METHODS

In this study, ultrasound localisation microscopy (ULM) was applied to image the cerebrovascular alterations of the transgenic female 5×FAD mouse model across different stages of disease progression: early (4 months), moderate (7 months), and late (12 months). Age-matched non-transgenic (non-Tg) littermates were used as controls. Immunohistology examinations were performed to evaluate the influence of disease progression on the β-amyloid (Aβ) load and microvascular alterations, including morphological changes and the blood-brain barrier (BBB) leakage.

FINDINGS

Our findings revealed a significant decline in both vascular density and flow velocity in the retrosplenial cortex of 5×FAD mice at an early stage, which subsequently became more pronounced in the visual cortex and hippocampus as the disease progressed. Additionally, we observed a reduction in vascular length preceding diminished flow velocities in cortical penetrating arterioles during the early stages. The quantification of vascular metrics derived from ULM imaging showed significant correlations with those obtained from vascular histological images. Immunofluorescence staining identified early vascular abnormalities in the retrosplenial cortex. As the disease advanced, there was an exacerbation of Aβ accumulation and BBB disruption in a regionally variable manner. The vascular changes observed through ULM imaging exhibited a negative correlation with amyloid load and were associated with the compromise of the BBB integrity.

INTERPRETATION

Through high-resolution, in vivo imaging of cerebrovasculature, this study reveals significant spatiotemporal dysfunction in cerebrovascular dynamics accompanying disease progression in a mouse model of AD, enhancing our understanding of its pathophysiology.

FUNDING

This study is supported by grants from National Key Research and Development Program of China (2020YFA0908800), National Natural Science Foundation of China (12074269, 82272014, 82327804, 62071310), Shenzhen Basic Science Research (20220808185138001, JCYJ20220818095612027, JCYJ20210324093006017), STI 2030-Major Projects (2021ZD0200500) and Guangdong Natural Science Foundation (2024A1515012591, 2024A1515011342).

Dynamic mode decomposition based Doppler monitoring of de novo cavitation induced by pulsed HIFU: an in vivo feasibility study

Pulsed high-intensity focused ultrasound (pHIFU) has the capability to induce de novo cavitation bubbles, offering potential applications for enhancing drug delivery and modulating tissue microenvironments. However, imaging and monitoring these cavitation bubbles during the treatment presents a challenge due to their transient nature immediately following pHIFU pulses. A planewave bubble Doppler technique demonstrated its potential, yet this Doppler technique used conventional clutter filter that was originally designed for blood flow imaging. Our recent study introduced a new approach employing dynamic mode decomposition (DMD) to address this in an ex vivo setting. This study demonstrates the feasibility of the application of DMD for in vivo Doppler monitoring of the cavitation bubbles in porcine liver and identifies the candidate monitoring metrics for pHIFU treatment. We propose a fully automated bubble mode identification method using k-means clustering and an image contrast-based algorithm, leading to the generation of DMD-filtered bubble images and corresponding Doppler power maps after each HIFU pulse. These power Doppler maps are then correlated with the extent of tissue damage determined by histological analysis. The results indicate that DMD-enhanced power Doppler map can effectively visualize the bubble distribution with high contrast, and the Doppler power level correlates with the severity of tissue damage by cavitation. Further, the temporal characteristics of the bubble modes, specifically the decay rates derived from DMD, provide information of the bubble dissolution rate, which are correlated with tissue damage level-slower rates imply more severe tissue damage.

RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.

Singular value decomposition with weighting matrix applied for optical-resolution photoacoustic microscopes

The prestige target selectivity and imaging depth of optical-resolution photoacoustic microscope (OR-PAM) have gained attentions to enable advanced intra-cellular visualizations. However, the broad-band nature of photoacoustic signals is prone to noise and artifacts caused by the inefficient light-to-pressure translation, resulting in poor image quality. The present study foresees application of singular value decomposition (SVD) to effectively extract the photoacoustic signals from these noise and artifacts. Although spatiotemporal SVD succeeded in ultrasound flow signal extraction, the conventional multi frame model is not suitable for data acquired with scanning OR-PAM due to the burden of accessing multiple frames. To utilize SVD on the OR-PAM, this study began with exploring SVD applied on multiple A-lines of photoacoustic signal instead of frames. Upon explorations, an obstacle of uncertain presence of unwanted singular vectors was observed. To tackle this, a data-driven weighting matrix was designed to extract relevant singular vectors based on the analyses of temporal-spatial singular vectors. Evaluation on the extraction capability by the SVD with the weighting matrix showed a superior signal quality with efficient computation against past studies. In summary, this study contributes to the field by providing exploration of SVD applied on A-line signals as well as its practical utilization to distinguish and recover photoacoustic signals from noise and artifact components.

Quantification of brain-wide vascular resistivity via ultrafast Doppler in human neonates helps early detection of white matter injury

Preterm birth is associated with cerebrovascular development disruption and can induce white matter injuries (WMI). Transfontanellar ultrasound Doppler is the most widely used clinical imaging technique to monitor neonatal cerebral vascularisation and haemodynamics based on vascular indexes such as the resistivity index (RI); however, it has poor predictive value for brain damage. Indeed, these RI measurements are currently limited to large vessels, leading to a very limited probing of the brain's vascularisation, which may hinder prognosis. Here we show that ultrafast Doppler imaging (UfD) enables simultaneous quantification, in the whole field of view, of the local RI and vessel diameter, even in small vessels. Combining both pieces of information, we defined two new comprehensive resistivity parameters of the vascular trees. First, we showed that our technique is more sensitive in the early characterisation of the RI modifications between term and preterm neonates and for the first time we could show that the RI depends both on the vessel diameter and vascular territory. We then showed that our parameters can be used for early prediction of WMI. Our results demonstrate the potential of UfD to provide new biomarkers and pave the way for continuous monitoring of neonatal brain resistivity.

Measurement of Wall Shear Rate Across the Entire Vascular Wall Using Ultrasound Speckle Decorrelation

OBJECTIVE

The accurate measurement of the wall shear rate (WSR) plays a crucial role in the early diagnosis of cardiovascular disease progression and acute events such as aneurysms and atherosclerotic plaque ruptures. To address this need, the speckle decorrelation (SDC) technique has been used to measure WSR based on the 2-D out-of-plane blood flow speed. This technique is particularly advantageous because it enables the use of a 1-D array transducer to measure WSR over the entire luminal area. This study aims to develop a region-based singular value decomposition (SVD) filtering technique that selectively suppresses clutter noise in the vascular region to measure WSR using SDC.

METHOD

Ultrasound simulations, in-vitro flow experiments, and an in-vivo human study were conducted to evaluate the feasibility of this method's clinical application.

RESULTS

The results demonstrated that WSR can be effectively measured across entire vascular walls using a conventional 1-D array transducer along with the proposed methodology.

CONCLUSION

This study successfully demonstrates a noninvasive and accurate SDC-based method for measuring vital vascular WSR. This approach holds significant promise for assessing vascular WSR in both healthy individuals and high-risk cardiovascular disease patients.

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.

Ultrasound Speckle Decorrelation Analysis-Based Velocimetry for 3D-Velocity-Components Measurement Using a 1D Transducer Array

Ultrasound velocimetry has been widely used for blood flow imaging. However, the flow measurements are constrained to resolve the in-plane 2D flow components when using a 1D transducer array. In this work, an ultrasound speckle decorrelation analysis-based velocimetry (3C-vUS) is proposed for 3D velocity components measurement using a 1D transducer array. The 3C-vUS theory is first derived and validated with numerical simulations and phantom experiments. The in vivo testing results show that 3C-vUS can accurately measure the blood flow 3D-velocity-components of the human carotid artery at arbitrary probe-to-vessel angles throughout the cardiac cycle. With such capability, the 3C-vUS will alleviate the requirement of operators and promote disease screening for blood flow-related disorders.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

Nonlinear simulation for contrast ultrasound imaging

Contrast ultrasound (CUS) has received much interest because of its sensitivity enhancement for blood flow imaging. However, there is still a lack of nonlinear simulation method for CUS, as conventional simulators cannot deal with the microbubble acoustic nonlinearity. In this paper, a nonlinear simulation method of CUS is developed based on a combination strategy of the k-space pseudospectral method and Rayleigh-Plesset Marmottant model. Different contrast pulse sequence strategies as well as the radial modulation imaging are simulated and compared using the proposed method. For blood flow imaging, simulations under different scenarios such as power Doppler and ultrasound localization microscopy are also carried out. Furthermore, a face-to-face comparison is performed between simulations and phantom experiments to validate the proposed method.

Vector-flow imaging of slowly moving ex vivo blood with photoacoustics and pulse-echo ultrasound

We present a technique called photoacoustic vector-flow (PAVF) to quantify the speed and direction of flowing optical absorbers at each pixel from acoustic-resolution PA images. By varying the receiving angle at each pixel in post-processing, we obtain multiple estimates of the phase difference between consecutive frames. These are used to solve the overdetermined photoacoustic Doppler equation with a least-squares approach to estimate a velocity vector at each pixel. This technique is tested in bench-top experiments and compared to simultaneous pulse-echo ultrasound vector-flow (USVF) on whole rat blood at speeds on the order of 1 mm/s. Unlike USVF, PAVF can detect flow without stationary clutter filtering in this experiment, although the velocity estimates are highly underestimated. When applying spatio-temporal singular value decomposition clutter filtering, the flow speed can be accurately estimated with an error of 16.8% for USVF and - 8.9% for PAVF for an average flow speed of 2.5 mm/s.

3D Single Vessel Fractional Moving Blood Volume (3D-svFMBV): Fully Automated Tissue Perfusion Estimation Using Ultrasound

Power Doppler ultrasound (PD-US) is the ideal modality to assess tissue perfusion as it is cheap, patient-friendly and does not require ionizing radiation. However, meaningful inter-patient comparison only occurs if differences in tissue-attenuation are corrected for. This can be done by standardizing the PD-US signal to a blood vessel assumed to have 100% vascularity. The original method to do this is called fractional moving blood volume (FMBV). We describe a novel, fully-automated method combining image processing, numerical modelling, and deep learning to estimate three-dimensional single vessel fractional moving blood volume (3D-svFMBV). We map the PD signals to a characteristic intensity profile within a single large vessel to define the standardization value at the high shear vessel margins. This removes the need for mathematical correction for background signal which can introduce error. The 3D-svFMBV was first tested on synthetic images generated using the characteristics of uterine artery and physiological ultrasound noise levels, demonstrating prediction of standardization value close to the theoretical ideal. Clinical utility was explored using 143 first-trimester placental ultrasound volumes. More biologically plausible perfusion estimates were obtained, showing improved prediction of pre-eclampsia compared with those generated with the semi-automated original 3D-FMBV technique. The proposed 3D-svFMBV method overcomes the limitations of the original technique to provide accurate and robust placental perfusion estimation. This not only has the potential to provide an early pregnancy screening tool but may also be used to assess perfusion of different organs and tumors.

The brain of fetuses with congenital diaphragmatic hernia shows signs of hypoxic injury with loss of progenitor cells, neurons, and oligodendrocytes

Congenital diaphragmatic hernia (CDH) is a birth defect characterized by incomplete closure of the diaphragm, herniation of abdominal organs into the chest, and compression of the lungs and the heart. Besides complications related to pulmonary hypoplasia, 1 in 4 survivors develop neurodevelopmental impairment, whose etiology remains unclear. Using a fetal rat model of CDH, we demonstrated that the compression exerted by herniated organs on the mediastinal structures results in decreased brain perfusion on ultrafast ultrasound, cerebral hypoxia with compensatory angiogenesis, mature neuron and oligodendrocyte loss, and activated microglia. In CDH fetuses, apoptosis was prominent in the subventricular and subgranular zones, areas that are key for neurogenesis. We validated these findings in the autopsy samples of four human fetuses with CDH compared to age- and sex-matched controls. This study reveals the molecular mechanisms and cellular changes that occur in the brain of fetuses with CDH and creates opportunities for therapeutic targets.

Functional ultrasound imaging of the human spinal cord

Utilizing the first in-human functional ultrasound imaging (fUSI) of the spinal cord, we demonstrate the integration of spinal functional responses to electrical stimulation. We record and characterize the hemodynamic responses of the spinal cord to a neuromodulatory intervention commonly used for treating pain and increasingly used for the restoration of sensorimotor and autonomic function. We found that the hemodynamic response to stimulation reflects a spatiotemporal modulation of the spinal cord circuitry not previously recognized. Our analytical capability offers a mechanism to assess blood flow changes with a new level of spatial and temporal precision in vivo and demonstrates that fUSI can decode the functional state of spinal networks in a single trial, which is of fundamental importance for developing real-time closed-loop neuromodulation systems. This work is a critical step toward developing a vital technique to study spinal cord function and effects of clinical neuromodulation.

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High-Intensity Focused Ultrasound Therapy

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated with tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and its optimization for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filter-singular value decomposition (SVD) and infinite impulse response (IIR) high-pass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high-pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

Adaptive higher-order singular value decomposition clutter filter for ultrafast Doppler imaging of coronary flow under non-negligible tissue motion

BACKGROUND AND OBJECTIVE

With the development of advanced clutter-filtering techniques by singular value decomposition (SVD) and leveraging favorable acquisition settings such as open-chest imaging by a linear high-frequency probe and plane waves, several studies have shown the feasibility of cardiac flow measurements during the entire cardiac cycle, ranging from coronary flow to myocardial perfusion. When applying these techniques in a routine clinical setting, using transthoracic ultrasound imaging, new challenges emerge. Firstly, a smaller aperture is needed that can fit between ribs. Consequently, diverging waves are employed instead of plane waves to achieve an adequate field of view. Secondly, to ensure imaging at a larger depth, the maximum pulse repetition frequency has to be reduced. Lastly, in comparison to the open-chest scenario, tissue motion induced by the heartbeat is significantly stronger. The latter complicates substantially the distinction between clutter and blood signals.

METHODS

This study investigates a strategy to overcome these challenges by diverging wave imaging with an optimal number of tilt angles, in combination with dedicated clutter-filtering techniques. In particular, a novel, adaptive, higher-order SVD (HOSVD) clutter filter, which utilizes spatial, temporal, and angular information of the received ultrasound signals, is proposed to enhance clutter and blood separation.

RESULTS

When non-negligible tissue motion is present, using fewer tilt angles not only reduces the decorrelation between the received waveforms but also allows for collecting more temporal samples at a given ensemble duration, contributing to improved Doppler performance. The addition of a third angular dimension enables the application of HOSVD, providing greater flexibility in selecting blood separation thresholds from a 3-D tensor. This differs from the conventional threshold selection method in a 2-D spatiotemporal space using SVD. Exhaustive threshold search has shown a significant improvement in Contrast and Contrast-to-Noise ratio for Power Doppler images filtered with HOSVD compared to the SVD-based clutter filter.

CONCLUSION

With the improved settings, the obtained Power Doppler images show the feasibility of measuring coronary flow under the influence of non-negligible tissue motion in both in vitro and ex vivo.

Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.

Ultrafast Doppler Imaging for Early Detection of Synovitis in Rheumatoid Arthritis

OBJECTIVE

Current medical ultrasound systems possess limited sensitivity in detecting slow and weak blood flow during the early stages of rheumatoid arthritis (RA), leading to potential misdiagnosis. Ultrafast Doppler is capable of detecting slow and weak flow. This study was aimed at evaluating the diagnostic value of ultrafast Doppler for RA.

METHODS

Thirty-three RA patients (19 established, 14 early stage) and 15 healthy participants were enrolled. A programmable imaging platform with ultrafast Doppler capability was used. The benchmark was a clinical system with conventional Doppler imaging. Standardized dorsal long-axis scanning of both wrists was performed. Both ultrafast and conventional power Doppler (PD) images were quantitatively analyzed with computer assistance and semiquantitatively scored with the Outcome Measures in Rheumatology (OMERACT) scoring system.

RESULTS

Ultrafast PD revealed more blood area than conventional PD in both RA wrists and healthy wrists. Ultrafast PD OMERACT was positive in 65 of 66 RA wrists and 26 of 30 healthy wrists (sensitivity [SEN] = 0.985, accuracy [ACC] = 0.719), while conventional PD OMERACT was positive in 28 of 66 RA wrists and 0 of 30 healthy wrists (SEN = 0.424, ACC = 0.604). Ultrafast PD revealed a higher synovial PD area, dilated vessels and PD brightness in RA wrists. Peak synovial PD brightness had the best diagnostic value for RA (area under the receiver operating characteristic curve = 0.802, SEN = 0.909, ACC = 0.813). For early-stage RA patients, ultrafast peak synovial PD brightness had higher sensitivity and accuracy than conventional PD indexes.

CONCLUSION

Ultrafast PD had an increase of 0.561 in sensitivity and 0.209 in accuracy when compared with conventional PD. With its high sensitivity, ultrafast PD can detect early synovitis and identify RA patients during the early phase.

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High Intensity Focused Ultrasound Therapy

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated to tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and optimized for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filters - singular value decomposition (SVD) and infinite impulse response (IIR) highpass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

Detection of Individual Microbubbles by Burst-Wave-Aided Contrast-Enhanced Active Doppler Ultrasonography

We propose burst-wave-aided, contrast-enhanced, active Doppler ultrasonography for visualizing lymph vessels. This technique forces ultrasound contrast agents (UCAs) to move using the acoustic radiation force induced by burst waves with low amplitude while suppressing their destruction. Using a flow phantom, we measured the average, decrease rate of echo intensity [i.e., pulse intensity integral (PII)], and the velocity of individual contrast agents, which directly affects the performance of imaging and tracking contrast agents under stationary flow conditions. Comparison with pulse-inversion Doppler without exposure to the burst wave demonstrated that the velocity of the contrast agents could be enhanced up to several tens of millimeters per second by the effect of the burst wave, maximizing the echo intensity extracted by a clutter filter. The contrast ratio (CR), defined as the ratio of the contrast echo to the phantom echo outside the channel, did not change appreciably, even when the lower cut-off velocity of the clutter filter was increased up to 10 mm/s. This implies a better robustness against the motion of the tissue. In addition, the performance for detecting contrast agents (i.e., echo intensity) was superior or similar to that of pulse-inversion Doppler, even in undesirable conditions where the flow had a velocity component in the opposite direction to that of the acoustic radiation force. The echo intensity was lower or the same as that in pulse-inversion Doppler, demonstrating the potential for suppressing the destruction of contrast agents and enabling long-term observations. From these results, we expect that the proposed method will be beneficial for visualizing lymph vessels.

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry

Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity field information of a blood flows by localizing and tracking speckle signals of red blood cells (RBCs) without using any contrast agents. However, DL-SRU can localize only a small part of the speckle signals of blood flow owing to ambiguity problems encountered in the classification of blood flow signals from ultrasound B-mode images and the building up of suitable datasets required for training artificial neural networks, as well as the structural limitations of the neural network itself. An artificial intelligence-based speckle featurization and localization (AI-SFL) framework is proposed in this study. It includes a machine learning-based algorithm for classifying blood flow signals from ultrasound B-mode images, dimensionality reduction for featurizing speckle patterns of the classified blood flow signals by approximating them with quantitative values. A novel and robust neural network (ResSU-net) is trained using the online data generation (ODG) method and the extracted speckle features. The super-resolution performance of the proposed AI-SFL and ODG method is evaluated and compared with the results of previous U-net and conventional data augmentation methods under in silico conditions. The predicted locations of RBCs by the AI-SFL and DL-SRU for speckle patterns of blood flow are applied to a PTV algorithm to measure quantitative velocity fields of the flow. Finally, the feasibility of the proposed AI-SFL framework for measuring real blood flows is verified under in vivo conditions.

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.

Superficial Bifurcated Microflow Phantom for High-Frequency Ultrasound Applications

OBJECTIVE

To evaluate and optimize high-frequency ultrasound (HFUS) imaging techniques that visualize the morphology of microscale vasculatures, many studies have used flow phantoms with straight channels. However, the previous phantoms lack the complexity of microvessels to simulate a realistic vascular environment in a shallow depth. This study was aimed at devising a new protocol for fabrication of a microflow phantom with bifurcated geometry at a superficial region.

METHODS

The proposed protocol involved the following features: (i) a bifurcated flow tract model 300 µm in diameter was debossed on the surface of a tissue slab made of polyvinyl alcohol cryogel, and (ii) a wall-less lumen was created via bonding tissue slabs to put a lid on the debossed flow tract. The structure of the created microflow phantom was evaluated using 2-D and 3-D power Doppler imaging with a 30 MHz HFUS modality.

RESULTS

Ultrasound imaging revealed that the desired flow tract with bifurcation was successfully created in the phantom at a depth of 2-5 mm from the ultrasound probe. The diameters of the flow tract measured in the axial direction were 307 ± 3.7 µm in the parent branch and 232 ± 18.2 and 256 ± 23.3 µm in the two daughter branches, respectively.

CONCLUSION

The experiments revealed that the proposed protocol for creating a microscale intricate flow tract with desired dimensions and depth is valid. This new phantom will facilitate further improvement in the ultrasound technologies for the precise visualization of superficial complex vasculatures such as those in skin layers.

Clutter filtering of angular domain data for contrast-free ultrafast microvascular imaging

Objective. Contrast-free microvascular imaging is clinically valuable for the assessment of physiological status and the early diagnosis of diseases. Effective clutter filtering is essential for microvascular visualization without contrast enhancement. Singular value decomposition (SVD)-based spatiotemporal filter has been widely used to suppress clutter. However, clinical real-time imaging relies on short ensembles (dozens of frames), which limits the implementation of SVD filtering due to the large error of eigen-correlated estimations and high dependence on optimal threshold when used in such ensembles.Approach. To address the above challenges of imaging in short ensembles, two optimized filters of angular domain data are proposed in this paper: grouped angle SVD (GA-SVD) and angular-coherence-based higher-order SVD (AC-HOSVD). GA-SVD applies SVD to the concatenation of all angular data to improve clutter rejection performance in short ensembles, while AC-HOSVD applies HOSVD to the angular data tensor and utilizes angular coherence in addition to spatial and temporal features for filtering. Feasible threshold selection strategies in each feature space are provided. The clutter rejection performance of the proposed filters and SVD was evaluated with Doppler phantom andin vivostudies at different cases. Moreover, the robustness of the filters was explored under wrong singular value threshold estimation, and their computational complexity was studied.Main results. Qualitative and quantitative results indicated that GA-SVD and AC-HOSVD can effectively improve clutter rejection performance in short ensembles, especially AC-HOSVD. Notably, the proposed methods using 20 frames had similar image quality to SVD using 100 frames.In vivostudies showed that compared to SVD, GA-SVD increased the signal-to-noise-ratio (SNR) by 6.03 dB on average, and AC-HOSVD increased the SNR by 8.93 dB on average. Furthermore, AC-HOSVD remained better power Doppler image quality under non-optimal thresholds, followed by GA-SVD.Significance. The proposed filters can greatly enhance contrast-free microvascular visualization in short ensembles and have potential for different clinical translations due to the performance differences.

The fundamental mechanisms of the Korotkoff sounds generation

Blood pressure measurement is the most widely performed clinical exam to predict mortality risk. The gold standard for its noninvasive assessment is the auscultatory method, which relies on listening to the so-called "Korotkoff sounds" in a stethoscope placed at the outlet of a pneumatic arm cuff. However, more than a century after their discovery, the origin of these sounds is still debated, which implies a number of clinical limitations. We imaged the Korotkoff sound generation in vivo at thousands of images per second using ultrafast ultrasound. We showed with both experience and theory that Korotkoff sounds are paradoxically not sound waves emerging from the brachial artery but rather shear vibrations conveyed in surrounding tissues by the nonlinear pulse wave propagation. When these shear vibrations reached the stethoscope, they were synchronous, correlated, and comparable in intensity with the Korotkoff sounds. Understanding this mechanism could ultimately improve blood pressure measurement and provide additional understanding of arterial mechanical properties.

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.

Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micrometer-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive postprocessing times. In this study, we present a contrast-free super-resolution power Doppler (CS-PD) technique that uses deep networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS-PD could achieve an approximate twofold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS-PD showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio (PSNR) of 25.52. Moreover, CS-PD was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS-PD was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep neural network also allows CS-PD to be implemented for real-time imaging. These features of CS-PD offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

A Coded Excitation Framework for High SNR Transcranial Ultrasound Imaging

Robust transcranial ultrasound imaging is difficult due to poor image quality. In particular, low signal-to-noise ratio (SNR) limits sensitivity to blood flow and has hindered clinical translation of transcranial functional ultrasound neuroimaging thus far. In this work, we present a coded excitation framework to increase SNR in transcranial ultrasound without negatively impacting frame rate or image quality. We applied this coded excitation framework in phantom imaging and showed SNR gains as large as 24.78 dB and signal-to-clutter ratio gains as high as 10.66 dB with a 65 bit code. We also analyzed how imaging sequence parameters can impact image quality and showed how coded excitation sequences can be designed to maximize image quality for a given application. In particular, we show that considering the number of active transmit elements and the transmit voltage is critical for coded excitation with long codes. Finally, we applied our coded excitation technique in transcranial imaging of ten adult subjects and showed an average SNR gain of 17.91 ± 0.96 dB without a significant increase in clutter using a 65 bit code. We also performed transcranial power Doppler imaging in three adult subjects and showed contrast and contrast-to-noise ratio improvements of 27.32 ± 8.08 dB and 7.25 ± 1.61 dB, respectively with a 65 bit code. These results show that transcranial functional ultrasound neuroimaging may be possible using coded excitation.

Multi-layered adaptive neoangiogenesis Intra-Operative quantification (MANIOQ)

Quantification of vascularization volume can provide valuable information for diagnosis and prognosis in vascular pathologies. It can be adapted to inform the surgical management of gliomas, aggressive brain tumors characterized by exuberant sprouting of new blood vessels (neoangiogenesis). Filtered ultrafast Doppler data can provide two main parameters: vascularization index (VI) and fractional moving blood volume (FMBV) that clinically reflect tumor micro vascularization. Current protocols lack robust, automatic, and repeatable filtering methods. We present a filtrating method called Multi-layered Adaptive Neoangiogenesis Intra-Operative Quantification (MANIOQ). First, an adaptive clutter filtering is implemented, based on singular value decomposition (SVD) and hierarchical clustering. Second a method for noise equalization is applied, based on the subtraction of a weighted noise profile. Finally, an in vivo analysis of the periphery of the B-mode hyper signal area allows to measure the vascular infiltration extent of the brain tumors. Ninety ultrasound acquisitions were processed from 23 patients. Compared to reference methods in the literature, MANIOQ provides a more robust tissue filtering, and noise equalization allows for the first time to keep axial and lateral gain compensation (TGC and LGC). MANIOQ opens the way to an intra-operative clinical analysis of gliomas micro vascularization.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.