Adaptive Clutter Demodulation for Non-Contrast Ultrasound Perfusion Imaging

Power-Doppler Ultrasonic Imaging of Peripheral Perfusion in Diabetic Mice

OBJECTIVE

We explored the capabilities of power-Doppler ultrasonic (PD-US) imaging without contrast enhancement for monitoring changes in muscle perfusion over time.

METHODS

Ischemic recovery was observed in healthy and type II diabetic male and female mice with and without exercise. In separate studies, perfusion was measured during and after 5-min ischemic periods and during four-week recovery periods following irreversible femoral ligation. A goal was to assess how well PD-US estimates tracked the diabetic-related changes in endothelial function that influenced perfusion.

RESULTS

The average perfusion recovery time following femoral ligation increased 47% in diabetic males and 74% in diabetic females compared with non-diabetic mice. Flow-mediated dilation in conduit arteries and the reactive hyperemia index in resistive vessels each declined by one half in sedentary diabetic mice compared with sedentary non-diabetic mice. We found that exercise reduced the loss of endothelial function from diabetes in both sexes. The reproducibility of perfusion measurements was limited primarily by our ability to select the same region in muscle and to effectively filter tissue clutter.

CONCLUSIONS/SIGNIFICANCE

PD-US measurements can precisely follow site-specific changes in skeletal muscle perfusion related to diabetes over time, which fills the need for techniques capable of regularly monitoring atherosclerotic changes leading to ischemic vascular pathologies.

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.

Combining Spatial Registration With Clutter Filtering for Power-Doppler Imaging in Peripheral Perfusion Applications

Power-Doppler ultrasonic (PD-US) imaging is sensitive to echoes from blood cell motion in the microvasculature but generally nonspecific because of difficulties with filtering nonblood-echo sources. We are studying the potential for using PD-US imaging for routine assessments of peripheral blood perfusion without contrast media. The strategy developed is based on an experimentally verified computational model of tissue perfusion that simulates typical in vivo conditions. The model considers directed and diffuse blood perfusion states in a field of moving clutter and noise. A spatial registration method is applied to minimize tissue motion prior to clutter and noise filtering. The results show that in-plane clutter motion is effectively minimized. While out-of-plane motion remains a strong source of clutter-filter leakage, those registration errors are readily minimized by straightforward modification of scanning techniques and spatial averaging.

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.

Microvascular Flow Imaging: A State-of-the-Art Review of Clinical Use and Promise

Vascular imaging with color and power Doppler is a useful tool in the assessment of various disease processes. Assessment of blood flow, from infarction and ischemia to hyperemia, in organs, neoplasms, and vessels, is used in nearly every US investigation. Recent developments in this area are sensitive to small-vessel low velocity flow without use of intravenous contrast agents, known as microvascular flow imaging (MVFI). MVFI is more sensitive in detection of small vessels than color, power, and spectral Doppler, reducing the need for follow-up contrast-enhanced US (CEUS), CT, and MRI, except when arterial and venous wash-in and washout characteristics would be helpful in diagnosis. Varying clinical applications of MVFI are reviewed in adult and pediatric populations, including its technical underpinnings. MVFI shows promise in assessment of several conditions including benign and malignant lesions in the liver and kidney, acute pathologic abnormalities in the gallbladder and testes, and superficial lymph nodes. Future potential of MVFI in different conditions (eg, endovascular repair) is discussed. Finally, clinical cases in which MVFI correlated and potentially obviated additional CEUS, CT, or MRI are shown.

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.

Spatiotemporal Coherence to Quantify Sources of Image Degradation in Ultrasonic Imaging

Thermal noise and acoustic clutter signals degrade ultrasonic image quality and contribute to unreliable clinical assessment. When both noise and clutter are prevalent, it is difficult to determine which one is a more significant contributor to image degradation because there is no way to separately measure their contributions in vivo. Efforts to improve image quality often rely on an understanding of the type of image degradation at play. To address this, we derived and validated a method to quantify the individual contributions of thermal noise and acoustic clutter to image degradation by leveraging spatial and temporal coherence characteristics. Using Field II simulations, we validated the assumptions of our method, explored strategies for robust implementation, and investigated its accuracy and dynamic range. We further proposed a novel robust approach for estimating spatial lag-one coherence. Using this robust approach, we determined that our method can estimate the signal-to-thermal noise ratio (SNR) and signal-to-clutter ratio (SCR) with high accuracy between SNR levels of -30 to 40 dB and SCR levels of -20 to 15 dB. We further explored imaging parameter requirements with our Field II simulations and determined that SNR and SCR can be estimated accurately with as few as two frames and sixteen channels. Finally, we demonstrate in vivo feasibility in brain imaging and liver imaging, showing that it is possible to overcome the constraints of in vivo motion using high-frame rate M-Mode imaging.

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.

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.

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.

Closed-Loop Low-Rank Echocardiographic Artifact Removal

Echocardiographic image sequences are frequently corrupted by quasi-static artifacts ("clutter") superimposed on the moving myocardium. Conventionally, localized blind source separation methods exploiting local correlation in the clutter have proven effective in the suppression of these artifacts. These methods use the spectral characteristics to distinguish the clutter from tissue and background noise and are applied exhaustively over the data set. The exhaustive application results in high computational complexity and a loss of useful tissue signal. In this article, we develop a closed-loop algorithm in which the clutter is first detected using an adaptively determined weighting function and then removed using low-rank estimation methods. We show that our method is adaptable to different low-rank estimators, by presenting two such estimators: sparse coding in the principal component domain and nuclear norm minimization. We compare the performance of our proposed method (CLEAR) with two methods: singular value filtering (SVF) and morphological component analysis (MCA). The performance was quantified in silico by measuring the error with respect to a known "ground truth" data set with no clutter for different combinations of moving clutter and tissue. Our method retains more tissue with a lower error of 3.88 ± 0.093 dB (sparse coding) and 3.47 ± 0.78 (nuclear norm) compared with the benchmark methods 8.5 ± 0.7 dB (SVF) and 9.3 ± 0.5 dB (MCA) particularly in instances where the rate of tissue motion and artifact motion is small (≤0.25 periods of center frequency per frame) while producing comparable clutter reduction performance. CLEAR was also validated in vivo by quantifying the tracking error over the cardiac cycle on five mouse heart data sets with synthetic clutter. CLEAR reduced the error by approximately 50%, compared with 25% for the SVF.

Blood Flow Imaging in the Neonatal Brain Using Angular Coherence Power Doppler

Using ultrasound to image small vessels in the neonatal brain can be difficult in the presence of strong clutter from the surrounding tissue and with a neonate motion during the scan. We propose a coherence-based beamforming method, namely the short-lag angular coherence (SLAC) beamforming that suppresses incoherent noise and motion artifacts in Ultrafast data, and we demonstrate its applicability to improve detection of blood flow in the neonatal brain. Instead of estimating spatial coherence across the receive elements, SLAC utilizes the principle of acoustic reciprocity to estimate angular coherence from the beamsummed signals from different plane-wave transmits, which makes it computationally efficient and amenable to advanced beamforming techniques, such as f-k migration. The SLAC images of a simulated speckle phantom show similar edge resolution and texture size as the matching B-mode images, and reduced random noise in the background. We apply SLAC power Doppler (PD) to free-hand imaging of neonatal brain vasculature with long Doppler ensembles and show that: 1) it improves visualization of small vessels in the cortex compared to conventional PD and 2) it can be used for tracking of blood flow in the brain over time, meaning it could potentially improve the quality of free-hand functional ultrasound.

3-D Intravascular Characterization of Blood Flow Velocity Fields with a Forward-Viewing 2-D Array

Risk stratification in coronary artery disease is an ongoing challenge for which few tools are available for quantifying physiology within coronary arteries. Recently, anatomy-driven computational fluid dynamic modeling has enabled the mapping of local flow dynamics in coronary stenoses, with derived parameters such as WSS exhibiting a strong capability for predicting adverse clinical events on a patient-specific basis. As cardiac catheterization is common in patients with coronary artery disease, minimally invasive technologies capable of identifying pathologic flow in situ in real time could have a significant impact on clinical decision- making. As a step toward in vivo quantification of slow flow near the arterial wall, proof-of-concept for 3-D intravascular imaging of blood flow dynamics is provided using a 118-element forward-viewing ring array transducer and a research ultrasound system. Blood flow velocity components are estimated in the direction of primary flow using an unfocused wave Doppler approach, and in the lateral and elevation directions, using a transverse oscillation approach. This intravascular 3-D vector velocity system is illustrated by acquiring real-time 3-D data sets in phantom experiments and in vivo in the femoral artery of a pig. The effect of the catheter on blood flow dynamics is also experimentally assessed in flow phantoms with both straight and stenotic vessels. Results indicate that 3-D flow dynamics can be measured using a small form factor device and that a hollow catheter design may provide minimal disturbance to flow measurements in a stenosis (peak velocity: 54.97 ± 2.13 cm/s without catheter vs. 51.37 ± 1.08 cm/s with hollow catheter, 6.5% error). In the future, such technologies could enable estimation of 3-D flow dynamics near the wall in patients already undergoing catheterization.

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.

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.

Combining Slow Flow Techniques With Adaptive Demodulation for Improved Perfusion Ultrasound Imaging Without Contrast

Noncontrast perfusion ultrasound imaging remains challenging due to spectral broadening of the tissue clutter signal caused by patient and sonographer hand motion. To address this problem, we previously introduced an adaptive demodulation scheme to suppress the bandwidth of tissue prior to high-pass filtering. Our initial implementation used single plane wave power Doppler imaging and a conventional tissue filter. Recent advancements in beamforming and tissue filtering have been proposed for improved slow flow imaging, including coherent flow power Doppler (CFPD) imaging and singular value decomposition (SVD) filtering. Here, we aim to evaluate adaptive demodulation in conjunction with improvements in beamforming and filtering using simulations, single-vessel phantoms, and an in vivo liver tumor embolization study. We show that simulated blood-to-background contrast-to-noise ratios are highest when using adaptive demodulation with CFPD and a 100-ms ensemble, which resulted in a 13.6-dB average increase in contrast-to-noise ratio compared to basic IIR filtering alone. We also show that combining adaptive demodulation with SVD and with CFPD + SVD results in 9.3- and 19-dB increases in contrast-to-noise ratios compared to IIR filtering alone at 700- and 500-ms ensembles for phantom data with 1- and 5-mm/s average flows, respectively. In general, combining techniques resulted in higher signal-to-noise, contrast-to-noise, and generalized contrast-to-noise ratios in both simulations and phantoms. Finally, adaptive demodulation with SVD resulted in the largest qualitative and quantitative changes in tumor-to-background contrast postembolization.

Non-invasive Small Vessel Imaging of Human Thyroid Using Motion-Corrected Spatiotemporal Clutter Filtering

Reliable assessment of small vessel blood flow in the thyroid, without using any contrast agents, can be challenging because of increased physiological motion resulting from its proximity to the pulsating carotid artery. In this study, we hypothesized that correction of tissue motion prior to singular value decomposition (SVD)-based clutter filtering can improve the coherency of the tissue components and, thus, may allow better clutter suppression and visualization of small vessels in the thyroid. We corroborated this hypothesis by conducting phantom and in vivo studies using a clinical ultrasound scanner implemented with compounded plane wave imaging. The phantom studies were conducted using a homogeneous tissue-mimicking phantom to study the impact of motion on the covariance of the spatiotemporal Doppler data, in the absence of blood activity. The non-invasive in vivo study was conducted on a 74-y-old woman with a thyroid nodule suspicious of malignancy. A rigid body-based motion correction was performed using tissue displacements obtained from 2-D normalized cross-correlation-based speckle tracking. Subsequently, the power Doppler images were computed using SVD-based spatiotemporal clutter filtering. The results from the phantom study revealed that motion can considerably reduce the covariance of the spatiotemporal data and, thus, increase the rank of the tissue components. When the phantom was subjected to a total translation displacement of 6 pixels over the entire ensemble, in each direction (axial and lateral), the covariance dropped by more than 25%. The results obtained from the non-invasive in vivo study indicated that visualization of small vessel blood flow improved with motion correction of the power Doppler ensemble. The contrast-to-noise ratio of the blood signal in motion-corrected power Doppler images was considerably higher (8.17 and 8.32 dB), compared with that obtained using the standard SVD approach at an optimal threshold (0.87 and 4.33 dB) and a lower singular value threshold (1.92 and 3.05 dB). Further, the covariance of the in vivo thyroid spatiotemporal data increased by approximately 10% with motion correction. These preliminary results indicate that motion correction can be used to improve the visualization of small vessel blood flow in the thyroid, without using any contrast agents. The results of this feasibility study were encouraging, and warrant further development and more in vivo validation in moving tissues and organs.

Multidimensional Clutter Filter Optimization for Ultrasonic Perfusion Imaging

Combinations of novel pulse-echo acquisitions and clutter filtering techniques can improve the sensitivity and the specificity of power Doppler (PD) images, thus reducing the need for exogenous contrast enhancement. We acquire echoes following bursts of Doppler pulse transmissions sparsely applied in regular patterns over long durations. The goal is to increase the sensitivity of the acquisition to slow disorganized patterns of motion from the peripheral blood perfusion. To counter a concomitant increase in clutter signal power, we arrange the temporal echo acquisitions into two data-array axes, combine them with a spatial axis for the tissue region of interest, and apply 3-D singular-value decomposition (SVD) clutter filtering. Successful separation of blood echoes from other echo signal sources requires that we partition the 3-D SVD core tensor. Unfortunately, the clutter and blood subspaces do not completely uncouple in all situations, so we developed a statistical classifier that identifies the core tensor subspace dominated by tissue clutter power. This paper describes an approach to subspace partitioning as required for optimizing PD imaging of peripheral perfusion. The technique is validated using echo simulation, flow-phantom data, and in vivo data from a murine melanoma model. We find that for narrow eigen-bandwidth clutter signals, we can routinely map phantom flows and tumor perfusion signals at speeds less than 3 mL/min. The proposed method is well suited to peripheral perfusion imaging applications.

ASAP: Super-Contrast Vasculature Imaging Using Coherence Analysis and High Frame-Rate Contrast Enhanced Ultrasound

The very high frame rate afforded by ultrafast ultrasound, combined with microbubble contrast agents, opens new opportunities for imaging tissue microvasculature. However, new imaging paradigms are required to obtain superior image quality from the large amount of acquired data while allowing real-time implementation. In this paper, we report a technique-acoustic sub-aperture processing (ASAP)-capable of generating very high contrast/signal-to-noise ratio (SNR) images of macro-and microvessels, with similar computational complexity to classical power Doppler (PD) imaging. In ASAP, the received data are split into subgroups. The reconstructed data from each subgroup are temporally correlated over frames to generate the final image. As signals in subgroups are correlated but the noise is not, this substantially reduces the noise floor compared to PD. Using a clinical imaging probe, the method is shown to visualize vessels down to $200~\mu \text{m}$ with a SNR of 10 dB higher than PD and to resolve microvascular flow/perfusion information in rabbit kidneys noninvasively in vivo at multiple centimeter depths. With careful filter design, the technique also allows the estimation of flow direction and the separation of fast flow from tissue perfusion. ASAP can readily be implemented into hardware/firmware for real-time imaging and can be applied to contrast enhanced and potentially noncontrast imaging and 3-D imaging.

Background Removal and Vessel Filtering of Noncontrast Ultrasound Images of Microvasculature

OBJECTIVE

Recent advances in ultrasound Doppler imaging have made it possible to visualize small vessels with diameters near the imaging resolution limits using spatiotemporal singular value thresholding of long ensembles of ultrasound data. However, vessel images derived based on this method present severe intensity variations and additional background noise that limits visibility and subsequent processing such as centerline extraction and morphological analysis. The goal of this paper is to devise a method to enhance vessel-background separation directly on the power Doppler images by exploiting blood echo-noise independence.

METHOD

We present a two-step algorithm to mitigate these adverse effects when using singular value thresholding for obtaining gross vasculature images. Our method comprises a morphological-based filtering for removing global and local background signals and a multiscale Hessian-based vessel enhancement filtering to further improve the vascular structures. We applied our method for in vivo imaging of the microvasculature of kidney in one healthy subject, liver in five healthy subjects, thyroid nodules in five patients, and breast tumors in five patients.

RESULTS

Singular value thresholding, top-hat filtering, and Hessian-based vessel enhancement filtering each provided an average peak-to-side level gain of 1.11, 18.55, and 2.26 dB, respectively, resulting in an overall gain of 21.92 dB when compared to the conventional power Doppler imaging using infinite impulse response filtering.

CONCLUSION

Singular value thresholding combined with morphological and Hessian-based vessel enhancement filtering provides a powerful tool for visualization of the deep-seated small vessels using long ultrasound echo ensembles without requiring any type of contrast enhancing agents.

SIGNIFICANCE

This method provides a fast and cost-effective modality for in vivo assessment of the microvasculature suitable for both clinical and preclinical applications.