Non-contrast agent based small vessel imaging of human thyroid using motion corrected power Doppler imaging

Quantitative Biomarkers Derived from a Novel, Contrast-Free Ultrasound, High-Definition Microvessel Imaging for Differentiating Choroidal Tumors

Angiogenesis has an essential role in the de novo evolution of choroidal melanoma as well as choroidal nevus transformation into melanoma. Differentiating early-stage melanoma from nevus is of high clinical importance; thus, imaging techniques that provide objective information regarding tumor microvasculature structures could aid accurate early detection. Herein, we investigated the feasibility of quantitative high-definition microvessel imaging (qHDMI) for differentiation of choroidal tumors in humans. This new ultrasound-based technique encompasses a series of morphological filtering and vessel enhancement techniques, enabling the visualization of tumor microvessels as small as 150 microns and extracting vessel morphological features as new tumor biomarkers. Distributional differences between the malignant melanomas and benign nevi were tested on 37 patients with choroidal tumors using a non-parametric Wilcoxon rank-sum test, and statistical significance was declared for biomarkers with p-values < 0.05. The ocular oncology diagnosis was choroidal melanoma (malignant) in 21 and choroidal nevus (benign) in 15 patients. The mean thickness of benign and malignant masses was 1.70 ± 0.40 mm and 3.81 ± 2.63 mm, respectively. Six HDMI biomarkers, including number of vessel segments (p = 0.003), number of branch points (p = 0.003), vessel density (p = 0.03), maximum tortuosity (p = 0.001), microvessel fractal dimension (p = 0.002), and maximum diameter (p = 0.003) exhibited significant distributional differences between the two groups. Contrast-free HDMI provided noninvasive imaging and quantification of microvessels of choroidal tumors. The results of this pilot study indicate the potential use of qHDMI as a complementary tool for characterization of small ocular tumors and early detection of choroidal melanoma.

Custom-made flow phantoms for quantitative ultrasound microvessel imaging

Morphologically realistic flow phantoms are essential experimental tools for quantitative ultrasound-based microvessel imaging. As new quantitative flow imaging tools are developed, the need for more complex vessel-mimicking phantoms is indisputable. In this article, we propose a method for fabricating phantoms with sub-millimeter channels consisting of branches and curvatures in various shapes and sizes suitable for quantifying vessel morphological features. We used different tissue-mimicking materials (TMMs) compatible with ultrasound imaging as the base and metal wires of different diameters (0.15-1.25 mm) to create wall-less channels. The TMMs used are silicone rubber, plastisol, conventional gelatin, and medical gelatin. Mother channels in these phantoms were made in diameters of 1.25 mm or 0.3 mm and the daughter channels in diameters 0.3 mm or 0.15 mm. Bifurcations were created by soldering wires together at branch points. Quantitative parameters were assessed, and accuracy of measurements from the ground truth were determined. Channel diameters were seen to have increased (76-270%) compared to the initial state in the power Doppler images, partly due to blood mimicking fluid pressure. Amongst the microflow phantoms made from the different TMMs, the medical gelatin phantom was selected as the best option for microflow imaging, fulfilling the objective of being easy to fabricate with high transmittance while having a speed of sound and acoustic attenuation close to human tissue. A flow velocity of 0.85 ± 0.01 mm/s, comparable to physiological flow velocity was observed in the smallest diameter phantom (medical gelatin branch) presented here. We successfully constructed more complex geometries, including tortuous and multibranch channels using the medical gelatin as the TMM. We anticipate this will create new avenues for validating quantitative ultrasound microvessel imaging techniques.

Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach

PURPOSE

Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo.

EXPERIMENTAL DESIGN

The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology.

RESULTS

Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%.

CONCLUSIONS

The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

Quantitative Biomarkers Derived from a Novel Contrast-Free Ultrasound High-Definition Microvessel Imaging for Distinguishing Thyroid Nodules

Low specificity in current ultrasound modalities for thyroid cancer detection necessitates the development of new imaging modalities for optimal characterization of thyroid nodules. Herein, the quantitative biomarkers of a new high-definition microvessel imaging (HDMI) were evaluated for discrimination of benign from malignant thyroid nodules. Without the help of contrast agents, this new ultrasound-based quantitative technique utilizes processing methods including clutter filtering, denoising, vessel enhancement filtering, morphological filtering, and vessel segmentation to resolve tumor microvessels at size scales of a few hundred microns and enables the extraction of vessel morphological features as new tumor biomarkers. We evaluated quantitative HDMI on 92 patients with 92 thyroid nodules identified in ultrasound. A total of 12 biomarkers derived from vessel morphological parameters were associated with pathology results. Using the Wilcoxon rank-sum test, six of the twelve biomarkers were significantly different in distribution between the malignant and benign nodules (all p < 0.01). A support vector machine (SVM)-based classification model was trained on these six biomarkers, and the receiver operating characteristic curve (ROC) showed an area under the curve (AUC) of 0.9005 (95% CI: [0.8279,0.9732]) with sensitivity, specificity, and accuracy of 0.7778, 0.9474, and 0.8929, respectively. When additional clinical data, namely TI-RADS, age, and nodule size were added to the features, model performance reached an AUC of 0.9044 (95% CI: [0.8331,0.9757]) with sensitivity, specificity, and accuracy of 0.8750, 0.8235, and 0.8400, respectively. Our findings suggest that tumor vessel morphological features may improve the characterization of thyroid nodules.

Morphometric analysis of tumor microvessels for detection of hepatocellular carcinoma using contrast-free ultrasound imaging: A feasibility study

Introduction

A contrast-free ultrasound microvasculature imaging technique was evaluated in this study to determine whether extracting morphological features of the vascular networks in hepatic lesions can be beneficial in differentiating benign and malignant tumors (hepatocellular carcinoma (HCC) in particular).

Methods

A total of 29 lesions from 22 patients were included in this work. A post-processing algorithm consisting of clutter filtering, denoising, and vessel enhancement steps was implemented on ultrasound data to visualize microvessel structures. These structures were then further characterized and quantified through additional image processing. A total of nine morphological metrics were examined to compare different groups of lesions. A two-sided Wilcoxon rank sum test was used for statistical analysis.

Results

In the malignant versus benign comparison, six of the metrics manifested statistical significance. Comparing only HCC cases with the benign, only three of the metrics were significantly different. No statistically significant distinction was observed between different malignancies (HCC versus cholangiocarcinoma and metastatic adenocarcinoma) for any of the metrics.

Discussion

Obtained results suggest that designing predictive models based on such morphological characteristics on a larger sample size may prove helpful in differentiating benign from malignant liver masses.

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.

Estimating the neovascularity of human finger tendon through high frequency ultrasound micro-Doppler imaging

OBJECTIVE

Neovascularization of injured tendons prolongs the proliferative phase of healing, but prolonged neovascularization may cause improper healing and pain. Currently, ultrasound Doppler imaging is used for measuring the neovascularization of injured tendons (e.g., Achilles tendon). However, the resolution of state-of-the-art clinical ultrasound machines is insufficient for visualizing the neovascularization in finger tendons. In this study, a high-resolution micro-Doppler imaging (HFDI) based on 40-MHz ultrafast ultrasound imaging was proposed for visualizing the neovascularization in injured finger tendons during multiple rehabilitation phases.

METHOD

The vessel visibility was enhanced through a block-wise singular value decomposition filter and several curvilinear structure enhancement strategies, including the bowler-hat transform and Hessian-based vessel enhancement filtering. HFDI was verified through small animal kidney and spleen imaging because the related vessel structure patterns of mice are well studied. Five patients with finger tendon injuries underwent HFDI examination at various rehabilitation phases after surgery (weeks 1156), and finger function evaluations were performed for comparisons.

RESULTS

The results of small animal experiments revealed that the proposed HFDI provides excellent microvasculature imaging performance; the contrast-to-noise ratio of HFDI was approximately 15 dB higher than that of the conventional singular value decomposition filter, and the minimum detectable vessel size for mouse kidney was 35 m without the use of contrast agent. In the human study, neovascularization was clearly observed in injured finger tendons during the early phase of healing (weeks 1121), but it regressed from week 52 to 56. Finger rehabilitation appears to help reduce neovascularization; neovascular density decreased by approximately 1.8%8.0% in participants after 4 weeks of rehabilitation.

CONCLUSION

The experimental results verified the performance of HFDI for microvasculature imaging and its potential for injured finger tendon evaluations.

Volumetric imaging and morphometric analysis of breast tumor angiogenesis using a new contrast-free ultrasound technique: a feasibility study

BACKGROUND

There is a strong correlation between the morphological features of new tumor vessels and malignancy. However, angiogenic heterogeneity necessitates 3D microvascular data of tumor microvessels for more reliable quantification. To provide more accurate information regarding vessel morphological features and improve breast lesion characterization, we introduced a quantitative 3D high-definition microvasculature imaging (q3D-HDMI) as a new easily applicable and robust tool to morphologically characterize microvasculature networks in breast tumors using a contrast-free ultrasound-based imaging approach.

METHODS

In this prospective study, from January 2020 through December 2021, a newly developed q3D-HDMI technique was evaluated on participants with ultrasound-identified suspicious breast lesions recommended for core needle biopsy. The morphological features of breast tumor microvessels were extracted from the q3D-HDMI. Leave-one-out cross-validation (LOOCV) was applied to test the combined diagnostic performance of multiple morphological parameters of breast tumor microvessels. Receiver operating characteristic (ROC) curves were used to evaluate the prediction performance of the generated pooled model.

RESULTS

Ninety-three participants (mean age 52 ± 17 years, 91 women) with 93 breast lesions were studied. The area under the ROC curve (AUC) generated with q3D-HDMI was 95.8% (95% CI 0.901-1.000), yielding a sensitivity of 91.7% and a specificity of 98.2%, that was significantly higher than the AUC generated with the q2D-HDMI (p = 0.02). When compared to q2D-HDMI, the tumor microvessel morphological parameters obtained from q3D-HDMI provides distinctive information that increases accuracy in differentiating breast tumors.

CONCLUSIONS

The proposed quantitative volumetric imaging technique augments conventional breast ultrasound evaluation by increasing specificity in differentiating malignant from benign breast masses.

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.

Ultrafast Power Doppler Imaging Using Frame-Multiply-and-Sum-Based Nonlinear Compounding

Ultrafast power Doppler imaging based on coherent compounding (UPDI-CC) has become a promising technique for microvascular imaging due to its high sensitivity to slow blood flows. However, since this method utilizes a limited number of plane-wave or diverging-wave transmissions for high-frame-rate imaging, it suffers from degraded image quality because of the low contrast resolution. In this article, an ultrafast power Doppler imaging method based on a nonlinear compounding framework, called frame-multiply-and-sum (UPDI-FMAS), is proposed to improve contrast resolution. In UPDI-FMAS, unlike conventional channel-domain delay-multiply-and-sum (DMAS) beamforming, the signal coherence is estimated based on autocorrelation function over plane-wave angle frames. To avoid phase distortion of blood flow signals during the autocorrelation process, clutter filtering is preferentially applied to individual beamformed plane-wave data set. Therefore, only coherent blood flow signals are emphasized, while incoherent background noise is suppressed. The performance of the UPDI-FMAS was evaluated with simulation, phantom, and in vivo studies. For the simulation and phantom studies with a constant laminar flow, the UPDI-FMAS showed improvements in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) to those of UPDI-CC, i.e., over 10 and 7 dB for 13 plane waves, respectively, and the performances were improved as the number of plane waves increased. Moreover, the enhancement of the image quality due to the increased SNR and CNR in UPDI-FMAS was more clearly depicted with the in vivo study, in which a human kidney and a tumor-bearing mouse were evaluated. These results indicate that the FMAS compounding can improve the image quality of UPDI for microvascular imaging without loss of temporal resolution.

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.

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.

Impact of imaging cross-section on visualization of thyroid microvessels using ultrasound: Pilot study

Non-invasive, contrast-free microvascular imaging of human thyroids can be potentially beneficial in reducing the large number of benign biopsies of suspicious nodules. However, motion incurred by thyroid due to its proximity to the pulsating carotid artery significantly impacts the visualization of blood flow in small vessels. Singular value based spatiotemporal clutter filtering (SVD-STF) improves the performance of tissue rejection in the presence of motion. However, despite effective clutter filtering, motion in thyroid imaging can impact coherent integration of the Doppler ensemble and degrade the visualization of the underlying vasculature. Recently studies have demonstrated that motion correction using 2D normalized cross-correlation based speckle tracking can address this issue, however, only in-plane motion can be tracked and corrected. Given the natural anatomical orientation of the rigid trachea, thyroid and the pulsating carotid artery, we hypothesize that imaging of thyroid microvessels may be more reliable in the longitudinal view than in the transverse. Specifically, distal presence of rigid trachea can limit out-of-plane motion in the longitudinal view. We tested this hypothesis on 48 acquisitions obtained from 24 thyroid patients having at least one suspicious nodule. In each patient, ultrasound images of the thyroid were acquired in both longitudinal and transverse views. Compounded plane-wave imaging was used to acquire the ultrasound images at high frame-rate, which is important for contrast-free small vessel blood flow imaging. Thyroid motion was tracked using 2D normalized cross-correlation based speckle tracking. Tissue clutter was rejected using singular value decomposition based spatiotemporal clutter filtering. The clutter-filtered Doppler ensemble was motion corrected prior to slow-time power Doppler integration. Signal-to-noise and contrast-to-noise ratios were computed to assess the improvement in quality of the power Doppler images. Out-of-plane motion was detected by estimating normalized ensemble cross-correlation coefficient. The results demonstrated that motion associated with the thyroid due to the carotid artery was primarily in the lateral direction, which could be estimated and corrected using 2D speckle tracking. However, the motion in the transverse view displayed increased speckle decorrelation. The average ensemble cross-correlation coefficient of the thyroid ultrasound images were significantly higher (p < 0.05) in the longitudinal view than in the transverse view. The largest improvement in SNR and CNR of the estimated PD images upon motion correction was observed in the longitudinal view (12.95 ± 3.76 dB and 16.48 ± 4.6 dB) than in the transverse view (3.72 ± 0.894 dB and 6.217 ± 1.689 dB). These preliminary results show that motion encountered by the thyroid due to carotid pulsations can be effectively tracked and corrected in the longitudinal view relative to transverse, which is important for reliably visualizing the underlying blood flow.

Adaptive background noise bias suppression in contrast-free ultrasound microvascular imaging

Non-invasive, contrast-free imaging of small vessel blood flow is diagnostically invaluable for detection, diagnosis and monitoring of disease. Recent advances in ultrafast imaging and tissue clutter-filtering have considerably improved the sensitivity of power Doppler (PD) imaging in detecting small vessel blood flow. However, suppression of tissue clutter exposes the depth-dependent time-gain compensated noise bias that noticeably degrades the PD image. We hypothesized that background suppression of PD images based on noise bias estimated from the entire clutter-filtered singular value spectrum can considerably improve flow signal visualization compared to currently existing techniques. To test our hypothesis, in vivo experiments were conducted on suspicious breast lesions in 10 subjects and deep-seated hepatic and renal microvasculatures in four healthy volunteers. Ultrasound PD images were acquired using a clinical ultrasound scanner, implemented with compounded plane wave imaging. The time gain compensated noise field was computed from the clutter-filtered Doppler ensemble (CFDE) based on its local spatio-temporal correlation, combined with low-rank signal estimation. Subsequently, the background bias in the PD images was suppressed by subtracting the estimated noise field. Background-suppressed PD images obtained using the proposed technique substantially improved visualization of the blood flow signal. The background bias in the noise suppressed PD images varied  <0.6 dB, independent of depth, which otherwise increased up to 13.8 dB. Further, the results demonstrated that the proposed technique efficaciously suppressed the background noise bias associated with smaller Doppler ensembles, which are challenging due to increased overlap between blood flow and noise components in the singular value spectrum. These preliminary results demonstrate the utility of the proposed technique to improve the visualization of small vessel blood flow in contrast-free PD images. The results of this feasibility study were encouraging, and warrant further development and additional in vivo validation.

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.