Eigen-based clutter filter design for ultrasound color flow imaging: a review

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.

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.

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.

Automatic Max-Likelihood Envelope Detection Algorithm for Quantitative High-Frame-Rate Ultrasound for Neonatal Brain Monitoring

OBJECTIVE

Post-operative brain injury in neonates may result from disturbed cerebral perfusion, but accurate peri-operative monitoring is lacking. High-frame-rate (HFR) cerebral ultrasound could visualize and quantify flow in all detectable vessels using spectral Doppler; however, automated quantification in small vessels is challenging because of low signal amplitude. We have developed an automatic envelope detection algorithm for HFR pulsed wave spectral Doppler signals, enabling neonatal brain quantitative parameter maps during and after surgery.

METHODS

HFR ultrasound data from high-risk neonatal surgeries were recorded with a custom HFR mode (frame rate = 1000 Hz) on a Zonare ZS3 system. A pulsed wave Doppler spectrogram was calculated for each pixel containing blood flow in the image, and spectral peak velocity was tracked using a max-likelihood estimation algorithm of signal and noise regions in the spectrogram, where the most likely cross-over point marks the blood flow velocity. The resulting peak systolic velocity (PSV), end-diastolic velocity (EDV) and resistivity index (RI) were compared with other detection schemes, manual tracking and RIs from regular pulsed wave Doppler measurements in 10 neonates.

RESULTS

Envelope detection was successful in both high- and low-quality arterial and venous flow spectrograms. Our technique had the lowest root mean square error for EDV, PSV and RI (0.46 cm/s, 0.53 cm/s and 0.15, respectively) when compared with manual tracking. There was good agreement between the clinical pulsed wave Doppler RI and HFR measurement with a mean difference of 0.07.

CONCLUSION

The max-likelihood algorithm is a promising approach to accurate, automated cerebral blood flow monitoring with HFR imaging in neonates.

Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning

Accurate measurement of blood flow velocity is important for the prevention and early diagnosis of atherosclerosis. However, due to the uncertainty of parameter settings, the autocorrelation velocimetry methods based on clutter filtering are prone to incorrectly filter out the near-wall blood flow signal, resulting in poor velocimetric accuracy. In addition, the Doppler coherent compounding acts as a low-pass filter, which also leads to low values of blood flow velocity estimated by the above methods. Motivated by this status quo, here we propose a deep learning estimator that combines clutter filtering and blood flow velocimetry based on the adaptive property of one-dimensional convolutional neural network (1DCNN). The estimator is operated by first extracting the blood flow signal from the original Doppler echo signal through an affine transformation of the 1D convolution, and then converting the extracted signal into the desired blood flow velocity using a linear transformation function. The effectiveness of the proposed method is verified by simulation as well as in vivo carotid artery data. Compared with typical velocimetry methods such as high-pass filtering (HPF) and singular value decomposition (SVD), the results show that the normalized root means square error (NRMSE) obtained by 1DCNN is reduced by 54.99 % and 53.50 % for forward blood flow velocimetry, and 70.99 % and 69.50 % for reverse blood flow velocimetry, respectively. Consistently, the in vivo measurements demonstrate that the goodness-of-fit of the proposed estimator is improved by 8.72 % and 4.74 % for five subjects. Moreover, the estimation time consumed by 1DCNN is greatly reduced, which costs only 2.91 % of the time of HPF and 12.83 % of the time of SVD. In conclusion, the proposed estimator is a better alternative to the current blood flow velocimetry, and is capable of providing more accurate diagnosis information for vascular diseases in clinical applications.

Spatially segmented SVD clutter filtering in cardiac blood flow imaging with diverging waves

Ultrafast ultrasound imaging enables the visualization of rapidly changing blood flow dynamics in the chambers of the heart. Singular value decomposition (SVD) filters outperform conventional high pass clutter rejection filters for ultrafast blood flow imaging of small and shallow fields of view (e.g., functional imaging of brain activity). However, implementing SVD filters can be challenging in cardiac imaging due to the complex spatially and temporally varying tissue characteristics. To address this challenge, we describe a method that involves excluding the proximal portion of the image (near the chest wall) and divides the reduced field of view into overlapped segments, within which tissue signals are expected to be spatially and temporally coherent. SVD filtering with automatic selection of cut-off singular vector orders to remove tissue and noise signals is implemented for each segment. Auto-thresholding is based on the coherence of spatial singular vectors, delineating tissue, blood, and noise subspaces within a spatial similarity matrix calculated for each segment. Filtered blood flow signals from the segments are reconstructed and then combined and Doppler processing is used to form a set of blood flow images. Preliminary experimental results suggest that the spatially segmented approach improves the separation of the tissue and blood subsets in the spatial similarity matrix so that automatic thresholding is significantly improved, and tissue clutter can then be rejected more effectively in cardiac ultrafast imaging, compared to using the full field of view. In the case studied, spatially segmented SVD improved the rate of correct automatic selection of thresholds from 78% to 98.7% for the investigated cases and improved the post-filter power of blood signals by an average of more than 10 dB during a cardiac cycle.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Monitoring Muscle Perfusion in Rodents During Short-Term Ischemia Using Power Doppler Ultrasound

OBJECTIVE

The aim of this work was to evaluate the reliability of power Doppler ultrasound (PD-US) measurements made without contrast enhancement to monitor temporal changes in peripheral blood perfusion.

METHODS

On the basis of pre-clinical rodent studies, we found that combinations of spatial registration and clutter filtering techniques applied to PD-US signals reproducibly tracked blood perfusion in skeletal muscle. Perfusion is monitored while modulating hindlimb blood flow. First, in invasive studies, PD-US measurements in deep muscle with laser speckle contrast imaging (LSCI) of superficial tissues made before, during and after short-term arterial clamping were compared. Then, in non-invasive studies, a pressure cuff was employed to generate longer-duration hindlimb ischemia. Here, B-mode imaging was also applied to measure flow-mediated dilation of the femoral artery while, simultaneously, PD-US was used to monitor downstream muscle perfusion to quantify reactive hyperemia. Measurements in adult male and female mice and rats, some with exercise conditioning, were included to explore biological variables.

RESULTS

PD-US methods are validated through comparisons with LSCI measurements. As expected, no significant differences were found between sexes or fitness levels in flow-mediated dilation or reactive hyperemia estimates, although post-ischemic perfusion was enhanced with exercise conditioning, suggesting there could be differences between the hyperemic responses of conduit and resistive vessels.

CONCLUSION

Overall, we found non-contrast PD-US imaging can reliably monitor relative spatiotemporal changes in muscle perfusion. This study supports the development of PD-US methods for monitoring perfusion changes in patients at risk for peripheral artery disease.

Region-based SVD processing of high-frequency ultrafast ultrasound to visualize cutaneous vascular networks

Observing alterations in cutaneous vasculature in response to any disease or pathology is considered as a potential diagnostic marker in the progression and cure of a disease. To observe skin morphologies and tissue conditions, high-frequency ultrasound (HFUS) has been used in dermatology, although its ability to selectively visualize micro-vessels is limited due to insufficient Doppler sensitivity to peripheral slow-speed blood flow. In recent studies, this issue has been improved by increasing the sensitivity of Doppler imaging to slow flow, leveraging advanced cutter filtering approaches based on singular value decomposition (SVD) techniques that aid to effectively extract flow signals overlapped with tissue echo signals. Nevertheless, in skin imaging, variations in flow speed, diameter, and depth of the blood vessels at different skin layers can make clutter filtering challenging because these variations are problematic in selecting the optimal cut-off value for the SVD filtering. In this study, we aimed to devise a novel region-based SVD filtering approach for ultrafast HFUS data to visualize cutaneous vascular networks. The proposed method divides the acquired high-framerate data into two regions based on B-mode cutaneous morphological identification (dermis layer and subcutaneous tissue). Singular value decomposition processing was performed on each region to effectively extract the desired flow signal, and the processed regions were merged to generate a single power Doppler image, thereby highlighting the appearance of a complete cutaneous vascular network. Finally, top-hat transform was applied to the power Doppler image to further suppress the background noises and enhances the visibility of the micro-vessels. Experimental observations of the human cutaneous circulation showed that the image quality (contrast-to-noise ratio) through the region-based SVD filtering was measured to be 4.1 dB (before top-hat filtering) and 5.2 dB (after top-hat filtering), which were improved from 3.4 dB and 4.0 dB obtained using the global SVD approach with and without top-hat filtering, respectively. We envisioned that this approach would provide diverse applications in the diagnosis of cutaneous disorders.

High-fidelity deep functional photoacoustic tomography enhanced by virtual point sources

Photoacoustic tomography (PAT), a hybrid imaging modality that acoustically detects the optical absorption contrast, is a promising technology for imaging hemodynamic functions in deep tissues far beyond the traditional optical microscopy. However, the most clinically compatible PAT often suffers from the poor image fidelity, mostly due to the limited detection view of the linear ultrasound transducer array. PAT can be improved by employing highly-absorbing contrast agents such as droplets and nanoparticles, which, however, have low clinical translation potential due to safety concerns and regulatory hurdles imposed by these agents. In this work, we have developed a new methodology that can fundamentally improve PAT's image fidelity without hampering any of its functional capability or clinical translation potential. By using clinically-approved microbubbles as virtual point sources that strongly and isotropically scatter the local pressure waves generated by surrounding hemoglobin, we can overcome the limited-detection-view problem and achieve high-fidelity functional PAT in deep tissues, a technology referred to as virtual-point-source PAT (VPS-PAT). We have thoroughly investigated the working principle of VPS-PAT by numerical simulations and in vitro phantom experiments, clearly showing the signal origin of VPSs and the resultant superior image fidelity over traditional PAT. We have also demonstrated in vivo applications of VPT-PAT for functional small-animal studies with physiological challenges. We expect that VPS-PAT can find broad applications in biomedical research and accelerated translation to clinical impact.

Fast and Robust Clutter Filtering in Ultrafast Echocardiography

Singular value decomposition (SVD)-based filters have become the norm for clutter filtering in ultrasound blood flow applications but are computationally expensive and susceptible to large and fast tissue motion. Randomized SVD (rSVD) has later been shown to successfully accelerate filtering of in vivo stationary tissues. However, little is known about its performance on ultrafast echocardiography, which produces thousands of frames to assess complex myocardial deformation and blood dynamics. Neither has its inherently robust randomized scheme been proven in any ultrasound blood flow imaging methods. This study thus proposed to employ rSVD as a fast and robust clutter filter for ultrafast echocardiograms prior to power Doppler analysis. Ultrafast echocardiograms of nine normal human hearts were acquired in vivo by our cascaded synthetic aperture imaging method. One subject was additionally scanned under four different sonographic signal-to-noise ratio (SNR) levels. Contrast ratio (CR) and contrast-to-noise ratio (CNR) of in vivo power Doppler images obtained from filtered ultrafast echocardiograms were calculated, and their mean and standard deviation within a cardiac cycle represented temporal average and variation of contrast resolution, respectively. Our in vivo results showed that rSVD accelerated clutter filtering by 12-fold and provided significantly better local contrast (mean CNR values: p < 0.001) while being equally effective (mean CR values: p = 0.20) compared with full-SVD. rSVD yielded smaller standard deviations of CR (1.32 dB vs. 5.49 dB) and CNR (1.27 dB vs. 5.49 dB) than full-SVD in the lowest SNR scenario, thus substantiating its superior robustness. Our findings suggest using rSVD in ultrafast echocardiographic blood dynamics analysis.

In vivoadaptive focusing for clinical contrast-enhanced transcranial ultrasound imaging in human

Objective. Imaging the human brain vasculature with high spatial and temporal resolution remains challenging in the clinic today. Transcranial ultrasound is still scarcely used for cerebrovascular imaging, due to low sensitivity and strong phase aberrations induced by the skull bone that only enable the proximal part major brain vessel imaging, even with ultrasound contrast agent injection (microbubbles).Approach. Here, we propose an adaptive aberration correction technique for skull bone aberrations based on the backscattered signals coming from intravenously injected microbubbles. Our aberration correction technique was implemented to image brain vasculature in human adults through temporal and occipital bone windows. For each subject, an effective speed of sound, as well as a phase aberration profile, were determined in several isoplanatic patches spread across the image. This information was then used in the beamforming process.Main results. This aberration correction method reduced the number of artefacts, such as ghost vessels, in the images. It improved image quality both for ultrafast Doppler imaging and ultrasound localization microscopy (ULM), especially in patients with thick bone windows. For ultrafast Doppler images, the contrast was increased by 4 dB on average, and for ULM, the number of detected microbubble tracks was increased by 38%.Significance. This technique is thus promising for better diagnosis and follow-up of brain pathologies such as aneurysms, arterial stenoses, arterial occlusions, microvascular disease and stroke and could make transcranial ultrasound imaging possible even in particularly difficult-to-image human adults.

Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.

Flow imaging using differential Golay encoded ultrasound

In our research we present a new method of differential compression of the Golay encoded ultrasound (DCGEU) in the standard beamforming mode to visualize the slow (<1cm/s) blood mimicking fluid flow in small diameter tubes. The proposed DCGEU method is based on synthesis of several subsequent B-mode frames acquired with certain time intervals (30 ms in this study) followed by the visualization of differential beamformed radio frequency (RF) echoes, which yielded the images of the scatterers moving slowly in the vessel and suppressing the static echoes outside the vessel. In order to extract small backscattered echoes from the vessel area we took an advantage of improved sensitivity of the complementary Golay coded sequences (CGCS). The validation of the proposed DCGEU method was carried out in two stages. In the first one, we compared the flow images in small tubes with a diameter of 1 mm and 2.5 mm, reconstructed from numerically simulated acoustic data for the standard transmission of short pulses and 16-bits long CGCS signals. In the second stage of the research, the experimental data were acquired in a flow phantom with silicone tubes with an internal diameter of 1.5 mm and 4.5 mm and a fluid flow velocity of 0.9 cm/s. The experiments were carried out using preprogrammed Verasonics Vantage™ research ultrasound system equipped with ALT L12-5/50 mm MHz linear array transducer with 7.8 MHz center frequency. It was evidenced both in simulations and experiments that the DCGEU provided a good flow image along the entire length of tubing with virtually angle independent detection in comparison with the conventional short pulse interrogation.

Blood Flow Quantification with High-Frame-Rate, Contrast-Enhanced Ultrasound Velocimetry in Stented Aortoiliac Arteries: In Vivo Feasibility

Local flow patterns influence stent patency, while blood flow quantification in stents is challenging. The aim of this study was to investigate the feasibility of 2-D blood flow quantification using high-frame-rate, contrast-enhanced ultrasound (HFR-CEUS) and particle image velocimetry (PIV), or echoPIV, in patients with aortoiliac stents. HFR-CEUS measurements were performed at 129 locations in 62 patients. Two-dimensional blood flow velocity fields were obtained using echoPIV. Visual inspection was performed by five observers to evaluate feasibility. The contrast-to-background ratio and average vector correlation were calculated and compared between stented and native vessel segments. Flow quantification with echoPIV was feasible in 128 of 129 locations (99%), with optimal quantification in 40 of 129 locations (31%). Partial quantification was achieved in 88 of 129 locations (68%), where one or multiple limiting issues occurred (not related to the stent) including loss of correlation during systole (57/129), short vessel segments (20/129), loss of contrast during diastole (20/129) and shadow regions (20/129). The contrast-to-background ratio and vector correlation were lower downstream in the imaged blood vessel, independent of the location of the stent. In conclusion, echoPIV was feasible in stents placed in the aortoiliac region, and the stents did not adversely affect flow tracking.

Development of an Imaging Framework for Visualization of Cutaneous Micro-Vasculature by using High Frequency Ultrafast Ultrasound Imaging

Visualization of cutaneous micro-vasculatures is a determined approach in the diagnosis of skin vascular disorders. Clinically, high frequency ultrasound (HFUS) modalities have been used for cutaneous morphological and structural imaging, but visualization of micro-vessels has always been remained a daunting task. These tiny structures might be visualized by devising a highly sensitive Doppler technique for HFUS systems. In this study, we proposed an imaging framework using HFUS (30 MHz) ultrafast Doppler imaging along with SVD clutter filtering that is proficient in detection of micro-scale circulation. The performance of the devised framework was examined on a 200-micron flow phantom made of poly-vinyl alcohol under four different flow rates (56 - 18 ul/min) and visualized the micro-structure with averaged detected diameter of 93 - 170 µm. The results indicated that the devised framework has sufficient sensitivity and resolvability to visualize the micro-vasculatures in dermis layer of skin (depth ≤ 4 mm). Clinical Relevance - This study brings an insight to visualize in-vivo cutaneous micro-vasculatures with ultrafast Doppler imaging in clinical applications for better assessment of cutaneous disorders.

Independent Component Analysis Filter for Small Vessel Contrast Imaging During Fast Tissue Motion

Suppressing tissue clutter is an essential step in blood flow estimation and visualization, even when using ultrasound contrast agents. Blind source separation (BSS)-based clutter filter for high-framerate ultrasound imaging has been reported to perform better in tissue clutter suppression than the conventional frequency-based wall filter and nonlinear contrast pulsing schemes. The most notable BSS technique, singular value decomposition (SVD) has shown compelling results in cases of slow tissue motion. However, its performance degrades when the tissue motion is faster than the blood flow speed, conditions that are likely to occur when imaging the small vessels, such as in the myocardium. Independent component analysis (ICA) is another BSS technique that has been implemented as a clutter filter in the spatiotemporal domain. Instead, we propose to implement ICA in the spatial domain where motion should have less impact. In this work, we propose a clutter filter with the combination of SVD and ICA to improve the contrast-to-background ratio (CBR) in cases where tissue velocity is significantly faster than the flow speed. In an in vitro study, the range of fast tissue motion velocity was 5-25 mm/s and the range of flow speed was 1-12 mm/s. Our results show that the combination of ICA and SVD yields 7-10 dB higher CBR than SVD alone, especially in the tissue high-velocity range. The improvement is crucial for cardiac imaging where relatively fast myocardial motions are expected.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part II: Phantom and In Vivo Experiments

Conventional color flow processing is associated with a high degree of operator dependence, often requiring the careful tuning of clutter filters and priority encoding to optimize the display and accuracy of color flow images. In a companion paper, we introduced a novel framework to adapt color flow processing based on local measurements of backscatter spatial coherence. Through simulation studies, the adaptive selection of clutter filters using coherence image quality characterization was demonstrated as a means to dynamically suppress weakly-coherent clutter while preserving coherent flow signal in order to reduce velocity estimation bias. In this study, we extend previous work to evaluate the application of coherence-adaptive clutter filtering (CACF) on experimental data acquired from both phantom and in vivo liver and fetal vessels. In phantom experiments with clutter-generating tissue, CACF was shown to increase the dynamic range of velocity estimates and decrease bias and artifact from flash and thermal noise relative to conventional color flow processing. Under in vivo conditions, such properties allowed for the direct visualization of vessels that would have otherwise required fine-tuning of filter cutoff and priority thresholds with conventional processing. These advantages are presented alongside various failure modes identified in CACF as well as discussions of solutions to mitigate such limitations.

A Pipeline for the Generation of Synthetic Cardiac Color Doppler

Color Doppler imaging (CDI) is the modality of choice for simultaneous visualization of myocardium and intracavitary flow over a wide scan area. This visualization modality is subject to several sources of error, the main ones being aliasing and clutter. Mitigation of these artifacts is a major concern for better analysis of intracardiac flow. One option to address these issues is through simulations. In this article, we present a numerical framework for generating clinical-like CDI. Synthetic blood vector fields were obtained from a patient-specific computational fluid dynamics CFD model. Realistic texture and clutter artifacts were simulated from real clinical ultrasound cineloops. We simulated several scenarios highlighting the effects of 1) flow acceleration; 2) wall clutter; and 3) transmit wavefronts, on Doppler velocities. As a comparison, an "ideal" color Doppler was also simulated, without these harmful effects. This synthetic dataset is made publicly available and can be used to evaluate the quality of Doppler estimation techniques. Besides, this approach can be seen as a first step toward the generation of comprehensive datasets for training neural networks to improve the quality of Doppler imaging.

Improving image contrast and accuracy in velocity estimation by convolution filters for intracardiac blood flow imaging

In this study, the point spread function (PSF) of an ultrasound imaging system was estimated and used as a reference signal in a filtering method for improvement of image quality. The PSF of the imaging system was estimated from measured echo signals from an imaging target. Convolution filters (including deconvolution) were used for improvement of image contrast and spatial resolution. Furthermore, the accuracy in estimation of velocity vectors was evaluated for investigation of the impact of the proposed filters on velocity estimation. In the phantom experiment, contrast of the B-mode image was improved from 76.4 dB to 81.1 dB and 77.8 dB using the convolution and deconvolution filters, respectively. Also, the two-dimensional (2D) velocity distribution in the phantom was estimated by the block matching method, and the bias error (BE) in the estimated lateral velocity was reduced from -19.7% to 2.16% and 2.29% using the convolution and the deconvolution filters, respectively.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part I: Simulation Studies

The appropriate selection of a clutter filter is critical for ensuring the accuracy of velocity estimates in ultrasound color flow imaging. Given the complex spatio-temporal dynamics of flow signal and clutter, however, the manual selection of filters can be a significant challenge, increasing the risk for bias and variance introduced by the removal of flow signal and/or poor clutter suppression. We propose a novel framework to adaptively select clutter filter settings based on color flow image quality feedback derived from the spatial coherence of ultrasonic backscatter. This framework seeks to relax assumptions of clutter magnitude and velocity that are traditionally required in existing adaptive filtering methods to generalize clutter filtering to a wider range of clinically-relevant color flow imaging conditions. In this study, the relationship between color flow velocity estimation error and the spatial coherence of clutter filtered channel signals was investigated in Field II simulations for a wide range of flow and clutter conditions. This relationship was leveraged in a basic implementation of coherence-adaptive clutter filtering (CACF) designed to dynamically adapt clutter filters at each imaging pixel and frame based on local measurements of spatial coherence. In simulation studies with known scatterer and clutter motion, CACF was demonstrated to reduce velocity estimation bias while maintaining variance on par with conventional filtering.

3D Transcranial Ultrasound Localization Microscopy in the Rat Brain with a Multiplexed Matrix Probe

OBJECTIVE

Ultrasound Localization Microscopy (ULM) provides images of the microcirculation in-depth in living tissue. However, its implementation in two-dimension is limited by the elevation projection and tedious plane-by-plane acquisition. Volumetric ULM alleviates these issues and can map the vasculature of entire organs in one acquisition with isotropic resolution. However, its optimal implementation requires many independent acquisition channels, leading to complex custom hardware.

METHODS

In this article, we implemented volumetric ultrasound imaging with a multiplexed 32 x 32 probe driven by a single commercial ultrasound scanner. We propose and compare three different sub-aperture multiplexing combinations for localization microscopy in silico and in vitro with a flow of microbubbles in a canal. Finally, we evaluate the approach for micro-angiography of the rat brain.The "light" combination allows a higher maximal volume rate than the "full" combination while maintaining the field of view and resolution.

RESULTS

In the rat brain, 100,000 volumes were acquired within 7 min with a dedicated ultrasound sequence and revealed vessels down to 31 m in diameter with flows from 4.3 mm/s to 28.4 mm/s.

CONCLUSION

This work demonstrates the ability to perform a complete angiography with unprecedented resolution in the living rats brain with a simple and light setup through the intact skull.

SIGNIFICANCE

We foresee that it might contribute to democratize 3D ULM for both preclinical and clinical studies.

Intraventricular Vector Flow Imaging with Blood Speckle Tracking in Adults: Feasibility, Normal Physiology and Mechanisms in Healthy Volunteers

This study examines the feasibility of blood speckle tracking for vector flow imaging in healthy adults and describes the physiologic flow pattern and vortex formation in relation to the wall motion in the left ventricle. The study included 21 healthy volunteers and quantified and visualized flow patterns with high temporal resolution down to a depth of 10-12 cm without the use of contrast agents. Intraventricular flow seems to originate during the isovolumetric relaxation with a propagation of blood from base to apex. With the E-wave, rapid inflow and vortex formation occurred on both sides of the valve basally. During diastasis the flow gathers in a large vortex before the pattern from the E-wave repeats during the A-wave. In isovolumetric contraction, the flow again gathers in a large vortex that seems to facilitate the flow out in the aorta during systole. No signs of a persistent systolic vortex were visualized. The geometry of the left ventricle and the movement of the AV-plane is important in creating vortices that are favorable for the blood flow and facilitate outflow. The quantitative measurements are in concordance with these findings, but the clinical interpretation must be evaluated in future clinical studies.

The Viability of 3-D Power Doppler Imaging Using Continuous Mechanical Translation: Simulation and Theoretical Analysis

Although conventional Doppler ultrasound is widely used for quantifying blood flow, it is restricted by its low sensitivity to detect slow flow. The incorporation of ultrafast ultrasound and spatial-temporal clutter filters can not only extensively boost the Doppler sensitivity to low-velocity slow flow but also facilitate the development of advanced 3-D Doppler techniques. In this work, we propose a novel 3-D Doppler method which extends 2-D imaging to 3-D through the continuous mechanical translation of a linear transducer. The viability of this method is assessed by simulations with the aids of a theoretical model. The combination of simulations and the theoretical model provides unique insights into the inherent mechanisms involved in the performance of this 3-D Doppler method and the roles of factors, such as tissue vibration characteristics, blood flow velocity, elevational point-spread-function profile, probe translating speed, and signal energy ratios.

Multidimensional Clutter Filtering of Aperture Domain Data for Improved Blood Flow Sensitivity

Singular value decomposition (SVD) is a valuable factorization technique used in clutter rejection filtering for power Doppler imaging. Conventionally, SVD is applied to a Casorati matrix of radio frequency data, which enables filtering based on spatial or temporal characteristics. In this article, we propose a clutter filtering method that uses a higher order SVD (HOSVD) applied to a tensor of aperture data, e.g., delayed channel data. We discuss temporal, spatial, and aperture domain features that can be leveraged in filtering and demonstrate that this multidimensional approach improves sensitivity toward blood flow. Further, we show that HOSVD remains more robust to short ensemble lengths than conventional SVD filtering. Validation of this technique is shown using Field II simulations and in vivo data.

In vivo 3D Power Doppler Imaging Using Continuous Translation and Ultrafast Ultrasound

The introduction of ultrafast ultrasound and spatiotemporal filtering has significantly improved the sensitivity of Doppler ultrasound imaging. This work describes the development of a novel 3D power Doppler imaging technique which uses a 1D-array ultrasound probe that mechanically translates at a constant speed. The continuous translation allows for a fast scan of a large 3D volume without requiring complex hardware. The technique was realized in a prototype and its feasibility illustrated using phantom and in-vivo kidney and breast lesion experiments. Although this 3D Doppler imaging technique is limited in some aspects, it enables power Doppler imaging of a large volume in a short acquisition time with less computational costs.

Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging

Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two subgroups. Coherent spatial compounding is performed within each subgroup, resulting in two compounded data sets. An SVD-based clutter filter is applied to each data set, followed by a correlation between the two data sets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7-10-dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor, and inflammatory bowel disease data sets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

Real-Time High Frame Rate Color Flow Mapping System

Plane wave (PW) transmission (TX) can be profitably used to improve the performance of color flow mapping (CFM) systems by increasing the autocorrelation ensemble length (EL) and/or the frame rate (FR). Although high-end scanners tend to include imaging schemes using PW TX and parallel receive beams, high frame rate (HFR) CFM has been so far experimentally implemented mostly through research platforms that transmit PWs and beamform/process the received channel data off-line. In this article, full real-time implementation of PW CFM with continuous-time clutter filtering and extended FR/EL is reported. The field-programmable gate arrays (FPGAs) and digital signal processors (DSPs) onboard the ULA-OP 256 research scanner were programmed to perform high-speed parallel beamforming and autocorrelation-based CFM processing, respectively. Different strategies were tested, in which the TX of PWs for CFM is either continuous or interleaved with the TX of packets of B-mode pulses. A fourth-order Chebyshev continuous-time high-pass filter with programmable cutoff frequency was implemented and its clutter rejection performance was positively compared with that obtained when operating on packet data. CFM FRs up to 575 were obtained. The possibility of programming the autocorrelation EL up to 64 permitted to detect flow with high sensitivity and accuracy (average relative errors down to 0.4% ± 8.4%). In vivo HFR movies are presented, showing the dynamics of flow in the common carotid artery, which highlight the presence of secondary flow components.

Dealiasing High-Frame-Rate Color Doppler Using Dual-Wavelength Processing

Doppler ultrasound is the premier modality to analyze blood flow dynamics in clinical practice. With conventional systems, Doppler can either provide a time-resolved quantification of the flow dynamics in sample volumes (spectral Doppler) or an average Doppler velocity/power [color flow imaging (CFI)] in a wide field of view (FOV) but with a limited frame rate. The recent development of ultrafast parallel systems made it possible to evaluate simultaneously color, power, and spectral Doppler in a wide FOV and at high-frame rates but at the expense of signal-to-noise ratio (SNR). However, like conventional Doppler, ultrafast Doppler is subject to aliasing for large velocities and/or large depths. In a recent study, staggered multi-pulse repetition frequency (PRF) sequences were investigated to dealias color-Doppler images. In this work, we exploit the broadband nature of pulse-echo ultrasound and propose a dual-wavelength approach for CFI dealiasing with a constant PRF. We tested the dual-wavelength bandpass processing, in silico, in laminar flow phantom and validated it in vivo in human carotid arteries ( n = 25 ). The in silico results showed that the Nyquist velocity could be extended up to four times the theoretical limit. In vivo, dealiased CFI were highly consistent with unfolded Spectral Doppler ( r²=0.83 , y=1.1x+0.1 , N=25 ) and provided consistent vector flow images. Our results demonstrate that dual-wavelength processing is an efficient method for high-velocity CFI.

Quantitative assessment of ensemble coherency in contrast-free ultrasound microvasculature imaging

Purpose

Contrast‐free visualization of microvascular blood flow (MBF) using ultrasound can play a valuable role in diagnosis and detection of diseases. In this study, we demonstrate the importance of quantifying ensemble coherence for robust MBF imaging. We propose a novel approach to quantify ensemble coherence by estimating the local spatiotemporal correlation (LSTC) image, and evaluate its efficacy through simulation and in vivo studies.

Methods

The in vivo patient studies included three volunteers with a suspicious breast tumor, 15 volunteers with a suspicious thyroid tumor, and two healthy volunteers for renal MBF imaging. The breast data displayed negligible prior motion and were used for simulation analysis involving synthetically induced motion, to assess its impact on ensemble coherency and motion artifacts in MBF images. The in vivo thyroid data involved complex physiological motion due to its proximity to the pulsating carotid artery, which was used to assess the in vivo efficacy of the proposed technique. Further, in vivo renal MBF images demonstrated the feasibility of using the proposed ensemble coherence metric for curved array‐based MBF imaging involving phase conversion. All ultrasound data were acquired at high imaging frame rates and the tissue signal was suppressed using spatiotemporal clutter filtering. Thyroid tissue motion was estimated using two‐dimensional normalized cross correlation‐based speckle tracking, which was subsequently used for ensemble motion correction. The coherence of the MBF image was quantified based on Casorati correlation of the Doppler ensemble.

Results

The simulation results demonstrated that an increase in ensemble motion corresponded with a decrease in ensemble coherency, which reciprocally degraded the MBF images. Further the data acquired from breast tumors demonstrated higher ensemble coherency than that from thyroid tumors. Motion correction improved the coherence of the thyroid MBF images, which substantially improved its visualization. The proposed coherence metrics were also useful in assessing the ensemble coherence for renal MBF imaging. The results also demonstrated that the proposed coherence metric can be reliably estimated from downsampled ensembles (by up to 90%), thus allowing improved computational efficiency for potential applications in real‐time MBF imaging.

Conclusions

This pilot study demonstrates the importance of assessing ensemble coherency in contrast‐free MBF imaging. The proposed LSTC image quantified coherence of the Doppler ensemble for robust MBF imaging. The results obtained from this pilot study are promising, and warrant further development and in vivo validation.

Plane-Wave Ultrasound Beamforming Through Independent Component Analysis

BACKGROUND AND OBJECTIVE

Beamforming in coherent plane-wave compounding (CPWC) is an essential step in maintaining high resolution, contrast and framerate. Adaptive methods have been designed to achieve this goal by estimating the apodization weights from echo traces acquired by several transducer elements.

METHODS

Herein, we formulate plane-wave beamforming as a blind source separation problem, where the output of each transducer element is considered as a non-independent observation of the field. As such, beamforming can be formulated as the estimation of an independent component out of the observations. We then adapt the independent component analysis (ICA) algorithm to solve this problem and reconstruct the final image.

RESULTS

The proposed method is evaluated on a set of simulations, real phantom, and in vivo data available from the plane-wave imaging challenge in medical ultrasound. Moreover, the results are compared with other well-known adaptive methods.

CONCLUSIONS

Results demonstrate that the proposed method simultaneously improves the resolution and contrast.

A Spatial Near-Field Clutter Reduction Filter Preserving Tissue Speckle in Echocardiography

Near-field (NF) clutter in echocardiography is depicted as a diffuse haze hindering the visualization of the myocardium and the blood-pool, thereby degrading its diagnostic value. Several clutter filters have been developed, which are limited in patients with contraction motion and rhythm anomalies, and in 3-D ultrasound (US). This study introduces a new NF clutter reduction method, which preserves US speckles required for strain imaging. The filter developed detects the NF clutter region in the spatial frequency domain. The filter employs an oriented, multiscale approach, and assumes the NF clutter to be predominantly present in the highest and lowest bandpass images. These bandpass images were filtered, whilst sparing features in the myocardium and NF clutter-free regions. The performance of the filter was assessed in a volunteer study, in ten 3-D apical and parasternal view acquisitions, and in a retrospective clinical study composed of 20 cardiac patients with different indications for echocardiography. The filter reduced NF clutter in all data sets, whilst preserving all or most of the myocardium. Additionally, it demonstrated a consistent enhancement of image quality, with an increase in contrast of 4.3 dB on average, and generated a clearer myocardial boundary distinction. Furthermore, the speckles were preserved according to the quality index based on local variance, the structural similarity index method, and normalized cross correlation values, being 0.82, 0.92, and 0.95 on average, respectively. Global longitudinal strain measurements on NF clutter reduced images were improved or equivalent compared to the original acquisitions, with an average increase in strain signal-to-noise ratio of 34%.

Retrospective Transmit Beamforming and Coherent Plane-Wave Compounding for Microvascular Doppler Imaging: A Comparison Study

Imaging blood flow in small vessels is of great clinical value for evaluating increased vascularization, potentially related to angiogenesis in cancer or inflammation processes in musculoskeletal disease. Using a traditional duplex imaging approach, a major challenge in color Doppler imaging is the limited amount of samples available for clutter filtering. Coherent plane-wave compounding (CPWC) enables a continuous high frame rate acquisition and improved image quality due to dynamic transmit focusing. However, the presence of moving scatterers in the image can lead to a loss in signal-to-noise ratio (SNR) and contrast. In this study, typical CPWC sequences for low-flow imaging were compared with retrospective transmit beamforming (RTB) sequences with similar frame rates and transmit power. The comparison was based on resolution, contrast, and SNR, using a stationary phantom, a flow phantom, a thread phantom, and in vivo recordings of blood vessels in the thyroid and kidney. A model was developed to estimate the difference in SNR between RTB and CPWC in the presence of static and moving scatterers while varying the transmit sequence parameters. The model predicted that RTB may yield an increased SNR compared with CPWC, especially for flow imaging, where the SNR difference reached 6 dB for a maximum velocity of 15 cm/s. The measured SNR values were in agreement with the predicted values, both in the case of stationary scatterers and for the flow phantom. We further demonstrated that reducing beam density to increase frame rate is associated with spatial undersampling (stripe) artifacts for RTB and grating lobes for CPWC. Both phantom and in vivo results indicate that transmit focusing may be beneficial in a low-flow imaging setup that, combined with adaptive clutter filtering, can yield superior microvascular imaging.

High contrast power Doppler imaging in side-viewing intravascular ultrasound imaging via angular compounding

The ability to assess likelihood of plaque rupture can determine the course of treatment in coronary artery disease. One indicator of plaque vulnerability is the development of blood vessels within the plaque, or intraplaque neovascularization. In order to visualize these vessels with increased sensitivity in the cardiac catheterization lab, a new approach for imaging blood flow in small vessels using side-viewing intravascular ultrasound (IVUS) is proposed. This approach based on compounding adjacent angular acquisitions was evaluated in tissue mimicking phantoms and ex vivo vessels. In phantom studies, the Doppler CNR increased from 3.3 ± 1.0 to 13 ± 2.6 (conventional clutter filtering) and from 1.9 ± 0.15 to 7.5 ± 1.1 (SVD filtering) as a result of applying angular compounding. When imaging flow at a rate of 5.6 mm/s in 200 µm tubes adjacent to the lumen of ex vivo porcine arteries, the Doppler CNR increased from 5.3 ± 0.95 to 7.2 ± 1.3 (conventional filtering) and from 23 ± 3.3 to 32 ± 6.7 (SVD filtering). Applying these strategies could allow increased sensitivity to slow flow in side-viewing intravascular ultrasound imaging.

Fast Randomized Singular Value Decomposition-Based Clutter Filtering for Shear Wave Imaging

The mechanical properties of soft tissues can be quantitatively characterized through the estimation of shear wave velocity (SWV) using various motion estimation methods, such as the commonly used block matching (BM) methods. However, such methods suffer from slow computational speed and many tunable parameters. In order to solve these problems, Butterworth filter-based clutter filter wave imaging (BW-CFWI) is recently proposed to detect the mechanical wave propagation by highlighting the tissue velocity induced by mechanical wave, without using any motion estimation methods. In this study, in order to improve the SWV estimation performance of the clutter filter wave imaging (CFWI) method, we propose singular value decomposition (SVD)-based clutter filter for CFWI (SVD-CFWI) and further accelerate it using a randomized SVD (rSVD)-based clutter filter (rSVD-CFWI). Homogeneous phantoms with different Young's moduli are used to investigate the influences of the cutoff order of singular value and iteration time on the performance of SWV estimation. An elasticity phantom with stepped cylindrical inclusions is tested for comparison of rSVD-CFWI, SVD-CFWI, BW-CFWI, and normalized cross-correlation (NCC)-based BM (NCC-BM). The performances of the proposed methods are also evaluated on data acquired from the bicipital muscle in vivo. The results of phantom experiments show that rSVD-CFWI and SVD-CFWI reconstruct SWV maps with improved shape of the inclusions. For the softest inclusion with a diameter of 10.40 mm, the contrast-to-noise ratios (CNRs) between the inclusions and background obtained with rSVD-CFWI (3.78 dB) and SVD-CFWI (3.71 dB) are higher than those obtained with BW-CFWI (0.55 dB) and NCC-BM (0.70 dB). For the stiffest inclusion with a diameter of 10.40 mm, higher CNRs are also achieved by rSVD-CFWI (5.68 dB) and SVD-CFWI (5.07 dB) than by BW-CFWI (2.92 dB) and NCC-BM (2.36 dB). In the in-vivo experiments, more homogeneous SWV maps and smaller standard deviations of SWVs are obtained with rSVD-CFWI and SVD-CFWI than with BW-CFWI and NCC-BM. Besides, RSVD-CFWI has lower computational complexity than SVD-CFWI and NCC-BM and has lower memory space requirement than SVD-CFWI. The computational speed of rSVD-CFWI is comparable to that of BW-CFWI and over 10 times higher than that of SVD-CFWI. Therefore, RSVD-CFWI is demonstrated to be a competitive tool for fast shear wave imaging.

Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Changes in spinal cord hemodynamics reflect modulation of spinal network with different parameters of epidural stimulation

In this study functional ultrasound (fUS) imaging has been implemented to explore the local hemodynamics response induced by electrical epidural stimulation and to study real-time in vivo functional changes of the spinal cord, taking advantage of the superior spatiotemporal resolution provided by fUS. By quantifying the hemodynamics and electromyographic response features, we tested the hypothesis that the temporal hemodynamics response of the spinal cord to electrical epidural stimulation could reflect modulation of the spinal circuitry and accordingly respond to the changes in parameters of electrical stimulation. The results of this study for the first time demonstrate that the hemodynamics response to electrical stimulation could reflect a neural-vascular coupling of the spinal cord. Response in the dorsal areas to epidural stimulation was significantly higher and faster compared to the response in ventral spinal cord. Positive relation between the hemodynamics and the EMG responses was observed at the lower frequencies of epidural stimulation (20 and 40 Hz), which according to our previous findings can facilitate spinal circuitry after spinal cord injury, compared to higher frequencies (200 and 500 Hz). These findings suggest that different mechanisms could be involved in spinal cord hemodynamics changes during different parameters of electrical stimulation and for the first time provide the evidence that neural-vascular coupling of the spinal cord circuitry could be related to specific organization of spinal cord vasculature and hemodynamics.

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.

Adaptive hybrid flow measurement of color Doppler and speckle image velocimetry

Color Doppler (CD) ultrasound has been commonly employed in biomedical field to get hemodynamic information. However, reliable diagnostic evaluation and criteria for vascular diseases may not be provided due to technical limitations of CD, including single-directional measurement, aliasing, and limited imaging conditions. In this study, adaptive hybrid (AH) scheme is proposed to enhance measurement accuracy of conventional CD. It can improve the accuracy of velocity field measurement by replacing erroneous vectors in the measured CD results with the correct vectors obtained from a speckle image velocimetry (SIV) technique. The performance of the proposed AH technique was validated through in vitro experiments for various flow rates and insonation angle conditions, comparing conventional velocimetry techniques. The in vitro experiments demonstrated that the AH technique could measure flow velocity with better accuracy than the CD with bias errors of below 0.7 mm/s. The clinical applicability of the AH was also validated by measuring venous flows at human lower extremity, checking constant volumetric flow rates. Flow rates measured by the AH were maintained along the vein, while the CD and SIV results varied. As a result, the AH can provide improved measurement accuracy without installing a new supplementary equipment. It would be effectively utilized for analyzing flow dynamics and diagnosing valve-related disease.

Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound

Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor.

A Spatial Coherence Beamformer Design for Power Doppler Imaging

Acoustic clutter is a primary source of image degradation in ultrasound imaging. In the context of flow imaging, tissue and acoustic clutter signals are often much larger in magnitude than the blood signal, which limits the sensitivity of conventional power Doppler in SNR-limited environments. This has motivated the development of coherence-based beamformers, including Coherent Flow Power Doppler (CFPD), which have demonstrated efficacy in mitigating sources of diffuse clutter. However, CFPD uses a measure of normalized coherence, which incurs a non-linear relationship between image intensity and the magnitude of the blood echo. As a result, CFPD is not a robust approach to study gradation of blood signal energy, which depicts the fractional moving blood volume. We propose the application of mutual intensity, rather than normalized coherence, to retain the clutter suppression capability inherent in coherence beamforming, while preserving the underlying signal energy. Feasibility of this approach was shown via Field II simulations, phantoms, and in vivo human liver data. In addition, we derive an adaptive statistical threshold for the suppression of residual noise signals. Overall, this beamformer design shows promise as an alternative technique to depict flow volume gradation in cluttered imaging environments.

Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system

Singular value decomposition (SVD)-based clutter filters can robustly reject the tissue clutter as compared with the conventional high pass filter-based clutter filters. However, the computational burden of SVD makes real time SVD-based clutter filtering challenging (e.g. frame rate at least 10-15 Hz with region of interest of about 4 × 4 cm²). Recently, we proposed an acceleration method based on randomized SVD (rSVD) clutter filtering and randomized spatial downsampling, which can significantly reduce the computational complexity without compromising the clutter rejection capability. However, this method has not been implemented on an ultrasound scanner and tested for its performance. In this study, we implement this acceleration method on a Verasonics scanner using a multi-core CPU architecture, and evaluate the selections of the imaging and processing parameters to enable real time micro-vessel imaging. The Blood-to-Clutter Ratio (BCR) performance was evaluated on a Verasonics machine with different settings of parameters such as block size and ensemble size. The demonstration of real time process was implemented on a 12-core CPU (downsampling factor of 12, 12-threads in this study) host computer. The processing time of the rSVD-based clutter filter was less than 30 ms and BCRs were higher than 20 dB as the block size, ensemble size and the rank of tissue clutter subspace were set as 30 × 30, 45 and 26 respectively. We also demonstrate that the micro-vessel imaging frame rate of the proposed architecture can reach approximately 22 Hz when the block size, ensemble size and the rank of tissue clutter subspace were set as 20 × 20 pixels, 45 and 26 respectively (using both images and supplementary videos). The proposed method may be important for real time 2D scanning of tumor microvessels in 3D to select and store the most representative 2D view with most abnormal micro-vessels for better diagnosis.

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.

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 Sub-pixel Motion-tracking Method with Out-of-plane Motion Detection for Precise Vascular Imaging

Ultrasound vascularity imaging provides important information for differential diagnosis of tumors. Peak-hold (PH) is a useful technique for precisely imaging small vessels by selecting a maximum brightness in each pixel through the frames obtained sequentially. To use PH successfully one needs motion compensation to reduce image blur, but out-of-plane motion cannot be avoided. To address this problem, we developed a sub-pixel motion-tracking method with out-of-plane motion detection (OPMD). It is a combination of the sum of the absolute differences (SAD) method and the Kanade-Lucas-Tomasi method and can be accurately applied to various motions. The value from OPMD (γ) is defined as a statistical value obtained from the distribution of residual values in the SAD procedure with the obtained frames. The value is ideally 0, and the frames having large γ are removed from the PH procedure. The accuracy of the proposed tracking method was found by a simulation study to be approximately 20 μm. We also found, through a phantom experiment, that the value of γ sensitively increased enough to detect out-of-plane motion. Most important, γ begins to increase before tracking errors occur. This suggests that OPMD can be used to predict tracking errors and effectively remove frames from the PH procedure. An in vivo experiment with a rabbit showed that the PH image obtained with motion tracking clearly revealed peripheral vessels that were blurred in the PH image obtained without motion tracking. We also found that the image quality becomes better when OPMD was used to remove frames including out-of-plane motion.

4-D Echo-Particle Image Velocimetry in a Left Ventricular Phantom

Left ventricular (LV) blood flow is an inherently complex time-varying 3-D phenomenon, where 2-D quantification often ignores the effect of out-of-plane motion. In this study, we describe high frame rate 4-D echocardiographic particle image velocimetry (echo-PIV) using a prototype matrix transesophageal transducer and a dynamic LV phantom for testing the accuracy of echo-PIV in the presence of complex flow patterns. Optical time-resolved tomographic PIV (tomo-PIV) was used as a reference standard for comparison. Echo-PIV and tomo-PIV agreed on the general profile of the LV flow patterns, but echo-PIV smoothed out the smaller flow structures. Echo-PIV also underestimated the flow rates at greater imaging depths, where the PIV kernel size and transducer point spread function were large relative to the velocity gradients. We demonstrate that 4-D echo-PIV could be performed in just four heart cycles, which would require only a short breath-hold, providing promising results. However, methods for resolving high velocity gradients in regions of poor spatial resolution are required before clinical translation.

Carotid artery ultrasound image analysis: A review of the literature

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Non-contrast power Doppler ultrasound imaging for early assessment of trans-arterial chemoembolization of liver tumors

Trans-arterial chemoembolization (TACE) is an important yet variably effective treatment for management of hepatic malignancies. Lack of response can be in part due to inability to assess treatment adequacy in real-time. Gold-standard contrast enhanced computed tomography and magnetic resonance imaging, although effective, suffer from treatment-induced artifacts that prevent early treatment evaluation. Non-contrast ultrasound is a potential solution but has historically been ineffective at detecting treatment response. Here, we propose non-contrast ultrasound with recent perfusion-focused advancements as a tool for immediate evaluation of TACE. We demonstrate initial feasibility in an 11-subject pilot study. Treatment-induced changes in tumor perfusion are detected best when combining adaptive demodulation (AD) and singular value decomposition (SVD) techniques. Using a 0.5 s (300-sample) ensemble size, AD + SVD resulted in a 7.42 dB median decrease in tumor power after TACE compared to only a 0.06 dB median decrease with conventional methods.

Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging

Ultrasound microvessel imaging (UMI) based on the combination of singular value decomposition (SVD) clutter filtering and ultrafast plane wave imaging has recently demonstrated significantly improved Doppler sensitivity, especially to small vessels that are invisible to conventional Doppler imaging. Practical implementation of UMI is hindered by the high computational cost associated with SVD and low blood signal-to-noise ratio (SNR) in deep regions of the tissue due to the lack of transmit focusing of plane waves. Concerning the high computational cost, an accelerated SVD clutter filtering method based on randomized SVD (rSVD) and randomized spatial downsampling (rSD) was recently proposed by our group, which showed the feasibility of real-time implementation of UMI. Concerning the low blood flow SNR in deep imaging regions, here we propose a noise suppression method based on noise debiasing that can be easily applied to the accelerated SVD method to bridge the gap between real-time implementation and high imaging quality. The proposed method experimentally measures the noise-induced bias by collecting the noise signal using the identical imaging sequence as regular UMI, but with the ultrasound transmission turned off. The estimated bias can then be subtracted from the original power Doppler (PD) image to obtain effective noise suppression. The feasibility of the proposed method was validated under different ultrasound imaging parameters [including transmitting voltages and time-gain compensation (TGC) settings] with a phantom experiment. The noise-debiased images showed an increase of up to 15.3 and 13.4 dB in SNR as compared to original PD images on the blood flow phantom and an in vivo human kidney data set, respectively. The proposed noise suppression method has negligible computational cost and can be conveniently combined with the previously proposed accelerated SVD clutter filtering technique to achieve high quality, real-time UMI imaging.

In Vivo Blood Velocity Vector Imaging Using Adaptive Velocity Compounding in the Carotid Artery Bifurcation

Visualization and quantification of blood flow are considered important for early detection of atherosclerosis and patient-specific diagnosis and intervention. As conventional Doppler imaging is limited to 1-D velocity estimates, 2-D and 3-D techniques are being developed. We introduce an adaptive velocity compounding technique that estimates the 2-D velocity vector field using predominantly axial displacements estimated by speckle tracking from dual-angle plane wave acquisitions. Straight-vessel experiments with a 7.8-MHz linear array transducer connected to a Verasonics Vantage ultrasound system revealed that the technique performed with a maximum velocity magnitude bias and angle bias of -3.7% (2.8% standard deviation) and -0.16° (0.41° standard deviation), respectively. In vivo, complex flow patterns were visualized in two healthy and three diseased carotid arteries and quantified using a vector complexity measure that increased with increasing wall irregularity. This measure could potentially be a relevant clinical parameter which might aid in early detection of atherosclerosis.