Dynamic Filtering of Adherent and Non-adherent Microbubble Signals Using Singular Value Thresholding and Normalized Singular Spectrum Area Techniques.

Ultrasound molecular imaging techniques rely on the separation and identification of three types of signals: static tissue, adherent microbubbles and non-adherent microbubbles. In this study, the image filtering techniques of singular value thresholding (SVT) and normalized singular spectrum area (NSSA) were combined to isolate and identify vascular endothelial growth factor receptor 2-targeted microbubbles in a mouse hindlimb tumor model (n = 24). By use of a Verasonics Vantage 256 imaging system with an L12-5 transducer, a custom-programmed pulse inversion sequence employing synthetic aperture virtual source element imaging was used to collect contrast images of mouse tumors perfused with microbubbles. SVT was used to suppress static tissue signals by 9.6 dB while retaining adherent and non-adherent microbubble signals. NSSA was used to classify microbubble signals as adherent or non-adherent with high accuracy (receiver operating characteristic area under the curve [ROC AUC] = 0.97), matching the classification performance of differential targeted enhancement. The combined SVT + NSSA filtering method also outperformed differential targeted enhancement in differentiating MB signals from all other signals (ROC AUC = 0.89) without necessitating destruction of the contrast agent. The results from this study indicate that SVT and NSSA can be used to automatically segment and classify contrast signals. This filtering method with potential real-time capability could be used in future diagnostic settings to improve workflow and speed the clinical uptake of ultrasound molecular imaging techniques.

A Targeted Molecular Localization Imaging Method Applied to Tumor Microvasculature.

OBJECTIVES: Ultrasound contrast agents, consisting of gas-filled microbubbles (MBs), have been imaged using several techniques that include ultrasound localization microscopy and targeted molecular imaging. Each of these techniques aims to provide indicators of the disease state but has traditionally been performed independently without co-localization of molecular markers and super-resolved vessels. In this article, we present a new imaging technology: a targeted molecular localization (TML) approach, which uses a single imaging sequence and reconstruction approach to co-localize super-resolved vasculature with molecular imaging signature to provide simultaneous anatomic and biological information for potential multiscale disease evaluation. MATERIALS AND METHODS: The feasibility of the proposed TML technique was validated in a murine hindlimb tumor model. Targeted molecular localization imaging was performed on 3 groups, which included control tissue (leg), tumor tissue, and tumor tissue after sunitinib an-tivascular treatment. Quantitative measures for vascular index (VI) and molecular index (MITML) were calculated from the microvasculature and TML images, respectively. In addition to these conventional metrics, a new metric unique to the TML technique, reporting the ratio of targeted molecular index to vessel surface, was assessed. RESULTS: The quantitative resolution results of the TML approach showed resolved resolution of the microvasculature down to 28.8 µm. Vascular index increased in tumors with and without sunitinib compared with the control leg, but the trend was not statistically significant. A decrease in MITML was observed for the tumor after treatment (P < 0.0005) and for the control leg (P < 0.005) compared with the tumor before treatment. Statistical differences in the ratio of molecular index to vessel surface were found between all groups: the control leg and tumor (P < 0.05), the control leg and tumor after sunitinib treatment (P < 0.05), and between tumors with and without sunitinib treatment (P < 0.001). CONCLUSIONS: These findings validated the technical feasibility of the TML method and pre-clinical feasibility for differentiating between the normal and diseased tissue states.

Validation of Normalized Singular Spectrum Area as a Classifier for Molecularly Targeted Microbubble Adherence.

Ultrasound molecular imaging is a diagnostic technique wherein molecularly targeted microbubble contrast agents are imaged to reveal disease markers on the blood vessel endothelium. Currently, microbubble adhesion to affected tissue can be quantified using differential targeted enhancement (dTE), which measures the late enhancement of adherent microbubbles through administration of destructive ultrasound pressures. In this study, we investigated a statistical parameter called the normalized singular spectrum area (NSSA) as a means to detect microbubble adhesion without microbubble destruction. We compared the signal differentiation capability of NSSA with matched dTE measurements in a mouse hindlimb tumor model. Results indicated that NSSA-based signal classification performance matches dTE when differentiating adherent microbubble from non-adherent microbubble signals (receiver operating characteristic area under the curve = 0.95), and improves classification performance when differentiating microbubble from tissue signals (p < 0.005). NSSA-based signal classification eliminates the need for destruction of contrast, and may offer better sensitivity, specificity and the opportunity for real-time microbubble detection and classification.

Imaging Performance of a Handheld Ultrasound System With Real-Time Computer-Aided Detection of Lumbar Spine Anatomy: A Feasibility Study.

OBJECTIVES: The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. MATERIALS AND METHODS: This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. RESULTS: The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%-98.1%) and a specificity of 85.5% (95% CI, 81.7%-88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%-97.7%) and specificity of 91.3% (95% CI, 83.6%-96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. CONCLUSIONS: The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.

Ultra-Low-Dose Ultrasound Molecular Imaging for the Detection of Angiogenesis in a Mouse Murine Tumor Model: How Little Can We See?

OBJECTIVES: The objective of this study was to evaluate the minimum microbubble dose for ultrasound molecular imaging to achieve statistically significant detection of angiogenesis in a mouse model. MATERIALS AND METHODS: The preburst minus postburst method was implemented on a Verasonics ultrasound research scanner using a multiframe compounding pulse inversion imaging sequence. Biotinylated lipid (distearoyl phosphatidylcholine-based) microbubbles that were conjugated with antivascular endothelial growth factor 2 (VEGFR2) antibody (MBVEGFR2) or isotype control antibody (MBControl) were injected into mice carrying adenocarcinoma xenografts. Different injection doses ranging from 5 × 10 to 1 × 10 microbubbles per mouse were evaluated to determine the minimum diagnostically effective dose. RESULTS: The proposed imaging sequence was able to achieve statistically significant detection (P < 0.05, n = 5) of VEGFR2 in tumors with a minimum MBVEGFR2 injection dose of only 5 × 10 microbubbles per mouse (distearoyl phosphatidylcholine at 0.053 ng/g mouse body mass). Nonspecific adhesion of MBControl at the same injection dose was negligible. In addition, the targeted contrast ultrasound signal of MBVEGFR2 decreased with lower microbubble doses, whereas nonspecific adhesion of MBControl increased with higher microbubble doses. CONCLUSIONS: The dose of 5 × 10 microbubbles per animal is now the lowest injection dose on record for ultrasound molecular imaging to achieve statistically significant detection of molecular targets in vivo. Findings in this study provide us with further guidance for future developments of clinically translatable ultrasound molecular imaging applications using a lower dose of microbubbles.

Optical Verification of Microbubble Response to Acoustic Radiation Force in Large Vessels With In Vivo Results.

OBJECTIVE: The objective of this study was to optically verify the dynamic behaviors of adherent microbubbles in large blood vessel environments in response to a new ultrasound technique using modulated acoustic radiation force. MATERIALS AND METHODS: Polydimethylsiloxane (PDMS) flow channels coated with streptavidin were used in targeted groups to mimic large blood vessels. The custom-modulated acoustic radiation force beam sequence was programmed on a Verasonics research scanner. In vitro experiments were performed by injecting a biotinylated lipid-perfluorobutane microbubble dispersion through flow channels. The dynamic response of adherent microbubbles was detected acoustically and simultaneously visualized using a video camera connected to a microscope. In vivo verification was performed in a large abdominal blood vessel of a murine model for inflammation with injection of biotinylated microbubbles conjugated with P-selectin antibody. RESULTS: Aggregates of adherent microbubbles were observed optically under the influence of acoustic radiation force. Large microbubble aggregates were observed solely in control groups without targeted adhesion. Additionally, the dispersion of microbubble aggregates were demonstrated to lead to a transient acoustic signal enhancement in control groups (a new phenomenon we refer to as "control peak"). In agreement with in vitro results, the control peak phenomenon was observed in vivo in a murine model. CONCLUSIONS: This study provides the first optical observation of microbubble-binding dynamics in large blood vessel environments with application of a modulated acoustic radiation force beam sequence. With targeted adhesion, secondary radiation forces were unable to produce large aggregates of adherent microbubbles. Additionally, the new phenomenon called control peak was observed both in vitro and in vivo in a murine model for the first time. The findings in this study provide us with a better understanding of microbubble behaviors in large blood vessel environments with application of acoustic radiation force and could potentially guide future beam sequence designs or signal processing routines for enhanced ultrasound molecular imaging.

Ultrasound-based measurement of molecular marker concentration in large blood vessels: a feasibility study.

Ultrasound molecular imaging has demonstrated efficacy in pre-clinical studies for cancer and cardiovascular inflammation. However, these techniques often require lengthy protocols because of waiting periods or additional control microbubble injections. Moreover, they are not capable of quantifying molecular marker concentration in human tissue environments that exhibit variable attenuation and propagation path lengths. Our group recently investigated a modulated acoustic radiation force-based imaging sequence, which was found to detect targeted adhesion independent of control measurements. In the present study, this sequence was tested against various experimental parameters to determine its feasibility for quantitative measurements of molecular marker concentration. Results indicated that measurements obtained from the sequence (residual-to-saturation ratio, Rresid) were independent of acoustic pressure and attenuation (p > 0.13, n = 10) when acoustic pressures were sufficiently low. The Rresid parameter exhibited a linear relationship with measured molecular marker concentration (R(2) > 0.94). Consequently, feasibility was illustrated in vitro, for quantification of molecular marker concentration in large vessels using a modulated acoustic radiation force-based sequence. Moreover, these measurements were independent of absolute acoustic reflection amplitude and used short imaging protocols (3 min) without control measurements.

Binding dynamics of targeted microbubbles in response to modulated acoustic radiation force.

Detection of molecular targeted microbubbles plays a foundational role in ultrasound-based molecular imaging and targeted gene or drug delivery. In this paper, an empirical model describing the binding dynamics of targeted microbubbles in response to modulated acoustic radiation forces in large vessels is presented and experimentally verified using tissue-mimicking flow phantoms. Higher flow velocity and microbubble concentration led to faster detaching rates for specifically bound microbubbles (p < 0.001). Higher time-averaged acoustic radiation force intensity led to faster attaching rates and a higher saturation level of specifically bound microbubbles (p < 0.05). The level of residual microbubble signal in targeted experiments after cessation of radiation forces was the only response parameter that was reliably different between targeted and control experiments (p < 0.05). A related parameter, the ratio of residual-to-saturated microbubble signal (Rresid), is proposed as a measurement that is independent of absolute acoustic signal magnitude and therefore able to reliably detect targeted adhesion independently of control measurements (p < 0.01). These findings suggest the possibility of enhanced detection of specifically bound microbubbles in real-time, using relatively short imaging protocols (approximately 3 min), without waiting for free microbubble clearance.

Shear forces from flow are responsible for a distinct statistical signature of adherent microbubbles in large vessels.

Real-time ultrasound-based targeted molecular imaging in large blood vessels holds promise for early detection and diagnosis of stroke risk by identifying early markers for atherosclerosis prior to plaque formation. Singular spectrum-based targeted molecular (SiSTM) imaging is a recently proposed method that uses changes in statistical dimensionality-quantified by a normalized singular spectrum area (NSSA)-to image receptor-ligand-bound adherent microbubbles. However, the precise physical mechanism responsible for the distinct statistical signature was previously unknown. In this study, in vitro flow phantom experiments were performed to elucidate the physical mechanism in large blood vessel environments. In the absence of flow, an increase in the NSSA of adherent microbubbles with respect to tissue was not observed with increased microbubble concentration or pulse length (p > .23; n  =  5) but was observed with increased flow rate (p < .01; n  =  10). When observing the dynamics of the adherent microbubble statistics, a good correlation was observed between the NSSA and the derivative of image intensity (R2 > .97). In addition, a monotonic relationship between the NSSA and decorrelation was demonstrated. These findings confirm the hypothesis that the statistical signature of adherent microbubbles is derived from frame-to-frame decorrelation, which is induced by flow shear forces.

Improved elevational and azimuthal motion tracking using sector scans.

Ultrasound data motion tracking is widely used to estimate relative tissue/transducer motion, for example in freehand 3-D imaging, in which successive 2-D ultrasound scan planes are registered in a 3-D volume. Speckle-tracking and decorrelation-based methods are used to estimate motion in the azimuthal and elevational planes. However, the performance of speckle-tracking is significantly degraded in sectorscan systems because of point-spread function rotation with lateral motion. In this paper, we develop a new method for joint azimuthal¿elevational motion estimation based on the complex correlation of individual IQ-demodulated sector-scan A-lines arising from tissue motion in 3-D space. We show that our method has performance benefits over both speckle-tracking and decorrelation-based tracking for motion estimation in sector-scan systems, particularly when there is both elevational and azimuthal motion. Motion-tracking efficacy is further demonstrated by improved freehand imaging of a known target (anatomically accurate 3-D-printed lumbar spine model) in a tissue-mimicking phantom, with an rms surface distance error of 1.2 mm, compared with 2.43 mm for conventional methods. These data indicate that the new algorithm is capable of improved tracking performance for sector scan systems, enabling effective freehand 3-D scanning.

Real-time targeted molecular imaging using singular value spectra properties to isolate the adherent microbubble signal.

Ultrasound-based real-time molecular imaging in large blood vessels holds promise for early detection and diagnosis of various important and significant diseases, such as stroke, atherosclerosis, and cancer. Central to the success of this imaging technique is the isolation of ligand-receptor bound adherent microbubbles from free microbubbles and tissue structures. In this paper, we present a new approach, termed singular spectrum-based targeted molecular (SiSTM) imaging, which separates signal components using singular value spectra content over local regions of complex echo data. Simulations were performed to illustrate the effects of acoustic target motion and harmonic energy on SiSTM imaging-derived measurements of statistical dimensionality. In vitro flow phantom experiments were performed under physiologically realistic conditions (2.7 cm s⁻¹ flow velocity and 4 mm diameter) with targeted and non-targeted phantom channels. Both simulation and experimental results demonstrated that the relative motion and harmonic characteristics of adherent microbubbles (i.e. low motion and large harmonics) yields echo data with a dimensionality that is distinct from free microbubbles (i.e. large motion and large harmonics) and tissue (i.e. low motion and low harmonics). Experimental SiSTM images produced the expected trend of a greater adherent microbubble signal in targeted versus non-targeted microbubble experiments (P < 0.05, n = 4). The location of adherent microbubbles was qualitatively confirmed via optical imaging of the fluorescent DiI signal along the phantom channel walls after SiSTM imaging. In comparison with two frequency-based real-time molecular imaging strategies, SiSTM imaging provided significantly higher image contrast (P < 0.001, n = 4) and a larger area under the receiver operating characteristic curve (P < 0.05, n = 4).

Assessing and improving acoustic radiation force image quality using a 1.5-D transducer design.

A 1.5-D transducer array was proposed to improve acoustic radiation force impulse (ARFI) imaging signal-to-noise ratio (SNRARFI) and image contrast relative to a conventional 1-D array. To predict performance gains from the proposed 1.5-D transducer array, an analytical model for SNRARFI upper bound was derived. The analytical model and 1.5-D ARFI array were validated using a finite element modelbased numerical simulation framework. The analytical model demonstrated good agreement with numerical results (correlation coefficient = 0.995), and simulated lesion images yielded a significant (2.92 dB; p < 0.001) improvement in contrast-tonoise ratio when rendered using the 1.5-D ARFI array.

The effects of transducer geometry on artifacts common to diagnostic bone imaging with conventional medical ultrasound.

The portability, low cost, and non-ionizing radiation associated with medical ultrasound suggest that it has potential as a superior alternative to X-ray for bone imaging. However, when conventional ultrasound imaging systems are used for bone imaging, clinical acceptance is frequently limited by artifacts derived from reflections occurring away from the main axis of the acoustic beam. In this paper, the physical source of off-axis artifacts and the effect of transducer geometry on these artifacts are investigated in simulation and experimental studies. In agreement with diffraction theory, the sampled linear-array geometry possessed increased off-axis energy compared with single-element piston geometry, and therefore, exhibited greater levels of artifact signal. Simulation and experimental results demonstrated that the linear-array geometry exhibited increased artifact signal when the center frequency increased, when energy off-axis to the main acoustic beam (i.e., grating lobes) was perpendicularly incident upon off-axis surfaces, and when off-axis surfaces were specular rather than diffusive. The simulation model used to simulate specular reflections was validated experimentally and a correlation coefficient of 0.97 between experimental and simulated peak reflection contrast was observed. In ex vivo experiments, the piston geometry yielded 4 and 6.2 dB average contrast improvement compared with the linear array when imaging the spinous process and interlaminar space of an animal spine, respectively. This work indicates that off-axis reflections are a major source of ultrasound image artifacts, particularly in environments comprising specular reflecting (i.e., bone or bone-like) objects. Transducer geometries with reduced sensitivity to off-axis surface reflections, such as a piston transducer geometry, yield significant reductions in image artifact.

The singular value filter: a general filter design strategy for PCA-based signal separation in medical ultrasound imaging.

A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.

Complex principal components for robust motion estimation.

Bias and variance errors in motion estimation result from electronic noise, decorrelation, aliasing, and inherent algorithm limitations. Unlike most error sources, decorrelation is coherent over time and has the same power spectrum as the signal. Thus, reducing decorrelation is impossible through frequency domain filtering or simple averaging and must be achieved through other methods. In this paper, we present a novel motion estimator, termed the principal component displacement estimator (PCDE), which takes advantage of the signal separation capabilities of principal component analysis (PCA) to reject decorrelation and noise. Furthermore, PCDE only requires the computation of a single principal component, enabling computational speed that is on the same order of magnitude or faster than the commonly used Loupas algorithm. Unlike prior PCA strategies, PCDE uses complex data to generate motion estimates using only a single principal component. The use of complex echo data is critical because it allows for separation of signal components based on motion, which is revealed through phase changes of the complex principal components. PCDE operates on the assumption that the signal component of interest is also the most energetic component in an ensemble of echo data. This assumption holds in most clinical ultrasound environments. However, in environments where electronic noise SNR is less than 0 dB or in blood flow data for which the wall signal dominates the signal from blood flow, the calculation of more than one PC is required to obtain the signal of interest. We simulated synthetic ultrasound data to assess the performance of PCDE over a wide range of imaging conditions and in the presence of decorrelation and additive noise. Under typical ultrasonic elasticity imaging conditions (0.98 signal correlation, 25 dB SNR, 1 sample shift), PCDE decreased estimation bias by more than 10% and standard deviation by more than 30% compared with the Loupas method and normalized cross-correlation with cosine fitting (NC CF). More modest gains were observed relative to spline-based time delay estimation (sTDE). PCDE was also tested on experimental elastography data. Compressions of approximately 1.5% were applied to a CIRS elastography phantom with embedded 10.4-mm-diameter lesions that had moduli contrasts of -9.2, -5.9, and 12.0 dB. The standard deviation of displacement estimates was reduced by at least 67% in homogeneous regions at 35 to 40 mm in depth with respect to estimates produced by Loupas, NC CF, and sTDE. Greater improvements in CNR and displacement standard deviation were observed at larger depths where speckle decorrelation and other noise sources were more significant.

Adaptive force sonorheometry for assessment of whole blood coagulation.

BACKGROUND: Viscoelastic diagnostics that monitor the hemostatic function of whole blood (WB), such as thromboelastography, have been developed with demonstrated clinical utility. By measuring the cumulative effects of all components of hemostasis, viscoelastic diagnostics have circumvented many of the challenges associated with more common tests of blood coagulation. METHODS: We describe a new technology, called sonorheometry, that adaptively applies acoustic radiation force to assess coagulation function in WB. The repeatability (precision) of coagulation parameters was assessed using citrated WB samples. A reference range of coagulation parameters, along with corresponding measurements from prothrombin time (PT) and partial thromboplastin time (PTT), were obtained from WB samples of 20 healthy volunteers. In another study, sonorheometry monitored anticoagulation with heparin (0-5 IU/ml) and reversal from varied dosages of protamine (0-10 IU/ml) in heparinized WB (2 IU/ml). RESULTS: Sonorheometry exhibited low CVs for parameters: clot initiation time (TC1), <7%; clot stabilization time (TC2), <6.5%; and clotting angle (theta), <3.5%. Good correlation was observed between clotting times, TC1 and TC2, and PTT (r=0.65 and 0.74 respectively; n=18). Linearity to heparin dosage was observed with average linearity r>0.98 for all coagulation parameters. We observed maximum reversal of heparin anticoagulation at protamine to heparin ratios of 1.4:1 from TC1 (P=0.6) and 1.2:1 from theta (P=0.55). CONCLUSIONS: Sonorheometry is a non-contact method for precise assessment of WB coagulation.

A novel ultrasound-based method to evaluate hemostatic function of whole blood.

BACKGROUND: Unregulated hemostasis represents a leading cause of mortality and morbidity in the developed world. Being able to recognize and quantify defects of the hemostatic process is critical to reduce mortality and implement appropriate treatment. METHODS: We describe a novel ultrasound-based technology, named sonorheometry, which can assess hemostasis function from a small sample of blood. Sonorheometry uses the phenomenon of acoustic radiation force to measure the dynamic changes in blood viscoelasticity during clot formation and clot dissolution. We performed in vitro experiments using whole blood samples of 1 ml to demonstrate that sonorheometry is indicative of hemostatic functions that depend on plasma coagulation factors, platelets, and plasma fibrinolytic factors. RESULTS: Sonorheometry measurements show titration effects to compounds known to alter the coagulation factors (GPRP peptide, 0 to 8 mmol/l), platelets (abciximab, 0 to 12 microg/ml), and fibrinolytic factors (urokinase, 0 to 200 U). Repeated measurements of blood samples from the same subjects yielded reproducibility errors on the order of 5%. CONCLUSIONS: These data indicate that sonorheometry accurately quantifies the functional role of the components of hemostasis in vitro.

Reduction of echo decorrelation via complex principal component filtering.

Ultrasound motion estimation is a fundamental component of clinical and research techniques that include color flow Doppler, spectral Doppler, radiation force imaging and ultrasound-based elasticity estimation. In each of these applications, motion estimates are corrupted by signal decorrelation that originates from nonuniform target motion across the acoustic beam. In this article, complex principal component filtering (PCF) is demonstrated as a filtering technique for dramatically reducing echo decorrelation in blood flow estimation and radiation force imaging. We present simulation results from a wide range of imaging conditions that illustrate a dramatic improvement over simple bandpass filtering in terms of overall echo decorrelation (< or =99.9% reduction), root mean square error (< or =97.3% reduction) and the standard deviation of displacement estimates (< or =97.4% reduction). A radiation force imaging technique, termed sonorheometry, was applied to fresh whole blood during coagulation, and complex PCF operated on the returning echoes. Sonorheometry was specifically chosen as an example radiation force imaging technique in which echo decorrelation corrupts motion estimation. At 2 min after initiation of blood coagulation, the average echo correlation for sonorheometry improved from 0.996 to 0.9999, which corresponded to a 41.0% reduction in motion estimation variance as predicted by the Cramer-Rao lower bound under reasonable imaging conditions. We also applied complex PCF to improve blood velocity estimates from the left carotid artery of a healthy 23-year-old male. At the location of peak blood velocity, complex PCF improved the correlation of consecutive echo signals from an average correlation of 0.94 to 0.998. The improved echo correlation for both sonorheometry and blood flow estimation yielded motion estimates that exhibited more consistent responses with less noise. Complex PCF reduces speckle decorrelation and improves the performance of ultrasonic motion estimation.

Robust principal component analysis and clustering methods for automated classification of tissue response to ARFI excitation.

We introduce a new method for automatic classification of acoustic radiation force impulse (ARFI) displacement profiles using what have been termed "robust" methods for principal component analysis (PCA) and clustering. Unlike classical approaches, the robust methods are less sensitive to high variance outlier profiles and require no a priori information regarding expected tissue response to ARFI excitation. We first validate our methods using synthetic data with additive noise and/or outlier curves. Second, the robust techniques are applied to classifying ARFI displacement profiles acquired in an atherosclerotic familial hypercholesterolemic (FH) pig iliac artery in vivo. The in-vivo classification results are compared with parametric ARFI images showing peak induced displacement and time to 67% recovery and to spatially correlated immunohistochemistry. Our results support that robust techniques outperform conventional PCA and clustering approaches to classification when ARFI data are inclusive of low to relatively high noise levels (up to 5 dB average signal-to-noise [SNR] to amplitude) but no outliers: for example, 99.53% correct for robust techniques vs. 97.75% correct for the classical approach. The robust techniques also perform better than conventional approaches when ARFI data are inclusive of moderately high noise levels (10 dB average SNR to amplitude) in addition to a high concentration of outlier displacement profiles (10% outlier content): for example, 99.87% correct for robust techniques vs. 33.33% correct for the classical approach. This work suggests that automatic identification of tissue structures exhibiting similar displacement responses to ARFI excitation is possible, even in the context of outlier profiles. Moreover, this work represents an important first step toward automatic correlation of ARFI data to spatially matched immunohistochemistry.