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Elastic degree of anisotropy (DoA) is a diagnostically relevant biomarker in muscle, kidney, breast, and other organs. Previously, elastic DoA was qualitatively assessed as the ratio of peak displacements (PD) achieved with the long-axis of a spatially asymmetric Acoustic Radiation Force Impulse (ARFI) excitation point spread function (PSF) aligned along versus across the axis of symmetry (AoS) in transversely isotropic materials. However, to better enable longitudinal and cross-sectional analyses, a quantitative measure of elastic DoA is desirable. In this study, qualitative ARFI PD ratios are converted to quantitative DoA, measured as the ratio of longitudinal over transverse shear elastic moduli, using a model empirically derived from Field II and finite element method (FEM) simulations. In silico, the median absolute percent error (MAPE) in ARFI-derived shear moduli ratio (SMR) was 1.75%, and predicted SMRs were robust to variations in transverse shear modulus, Young's moduli ratio, speed of sound, attenuation, density, and ARFI excitation PSF dimension. Further, ARFI-derived SMRs distinguished two materials when the true SMRs of the compared materials differed by as little as 10%. Experimentally, ARFI-derived SMRs linearly correlated with the corresponding ratios measured by Shear Wave Elasticity Imaging (SWEI) in excised pig skeletal muscle ( [Formula: see text], MAPE = 13%) and in pig kidney, in vivo ( [Formula: see text], MAPE = 5.3%). These results demonstrate the feasibility of using the ARFI PD to quantify elastic DoA in biological tissues.

Degree of anisotropy (DoA) of mechanical properties has been assessed as the ratio of Acoustic Radiation Force Impulse (ARFI) induced peak displacements (PDs) achieved using spatially asymmetric point spread functions (PSFs) that are rotated 90° to each other. Such PSF rotation has been achieved by manually rotating a linear array transducer, but manual rotation is cumbersome and prone to misalignment errors and higher variability in measurements. The purpose of this work is to evaluate the feasibility of electronic PSF rotation using a three-row transducer, which will reduce variability in DoA assessment. A Siemens 9L4, with 3×192 elements, was simulated in Field II to generate spatially asymmetric ARFI PSFs that were electronically rotated 63° from each other. Then, using the finite element method (FEM), peak displacements (PD) due to the ARFI excitation PSFs in 42 elastic, incompressible, transversely isotropic (TI) materials with shear moduli ratios of 1.0 to 6.0 were modeled. Finally, the ratio of PDs achieved using the two rotated PSFs was evaluated to assess elastic DoA. DoA increased with increasing shear moduli ratios and distinguished materials with 17% or greater difference in shear moduli ratios (Wilcoxon, p < 0.05). Experimentally, the ratio of PDs achieved using ARFI PSF rotated 63° from each other distinguished the biceps femoris muscle from two pigs, which had median shear moduli ratios of 4.25 and 3.15 as assessed by Shear Wave Elasticity Imaging (SWEI). These results suggest that ARFI-based DoA assessment can be achieved without manual transducer rotation using a three-row transducer capable of electronically rotating PSFs by 63°. It is expected that electronic PSF rotation will facilitate data acquisitions and improve the reproducibility of elastic anisotropy assessments.

Stroke is commonly caused by thromboembolic events originating from atherosclerotic plaques in the carotid vasculature. To improve stroke risk prediction by delineating vulnerable plaque features, our group has developed ARFI Variance of Acceleration log(VoA) imaging. We herein evaluate the ability of trained, blinded readers to interpret in vivo ARFI log(VoA) images of human carotid plaques, with validation by matched histology. Patients undergoing carotid endarterectomy were imaged using a Siemens S3000 and a 9L4 transducer. From 21 plaques, one was excluded due to specimen damage during surgery. Parametric 2D images of ARFI log(VoA) were rendered and evaluated by 3 neuroradiologists, 1 abdominal radiologist, 1 sonographer, and 1 pathologist, all blinded to the histological gold standard. Receiver operating characteristic (ROC) curve analysis was performed, and area under the curve (AUC) was taken as a metric of performance for detecting lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Reader log(VoA) outcomes were compared to those achieved using ARFI peak displacement (PD) in a comparable prior study. Average AUC performance for plaque features were as follows: CAL, 0.77; COL, 0.76; LRNC, 0.79; IPH, 0.82. Grouping the stiff (COL and CAL) and soft (LRNC and IPH) features together increased average AUCs to 0.86 for stiff and 0.87 for soft. These log(VoA) AUC outcomes were higher than those achieved by blinded readers evaluating ARFI PD images. This study shows the relevance of ARFI log(VoA) imaging, as interpreted by trained, blinded readers, to delineating the structure and composition of carotid atherosclerotic plaque in humans, in vivo.

Rupture potential of atherosclerotic plaques in carotid arteries is conferred by both composition and structure of plaques. Previous studies have shown that from in vivo collected data, carotid plaque components such as collagen, calcium, necrotic core and intraplaque hemorrhage can be automatically detected by an ARFI imaging-derived machine learning classifier. Automatic classification considered normalized cross-correlation measurements of ARFI-induced displacement, signal-to-noise ratio and cross-correlation coefficients from an on-axis ARFI acquisition. We now extend our prior work by hypothesizing that using multiple ARFI data forms improves plaque feature detection and FC thickness measurement in human carotid plaques, relative to a single form of ARFI data. Carotid plaques were imaged in vivo prior to surgery in 20 patients undergoing carotid endarterectomy (CEA), and extracted plaque specimens were harvested after CEA for histological processing. ARFI data were acquired with cardiac gating to diastole with push (DP) and to systole without push (SNP) using fundamental low (FL), fundamental high (FH), and harmonic (H) tracking frequencies. Combinations of the resulting displacement profiles were used as inputs to the SVM classifier. The classifier was evaluated by 5-fold cross-validation, with the histological samples acting as gold standards. From the output SVM likelihood matrices, ROC curves were calculated for separating collagen from calcium and lipid-rich necrotic core from intraplaque hemorrhage. For all examined plaques, DP+FH+H achieved the highest average AUC of 0.926 (Sensitivity = 0.932, Specificity = 0.915) but required 2 acquisitions. The best data combination requiring only one acquisition (DP+FH) achieved an AUC of 0.917 (Sensitivity = 0.902, Specificity = 0.880). These results suggest that using multiple forms of frequency and gating of ARFI data as inputs to an automatic classifier improves discrimination of carotid plaque components, relevant for rupture vulnerability assessment.

Quantitative Viscoelastic Response (QVisR) ultrasound uses in silico on-axis VisR displacement profiles to estimate the elastic and viscous moduli of an interrogated material. This work studies the effect of varying the radio frequency (RF) data signal-to-noise ratio (SNR) on QVisR's estimates. White Gaussian noise was added to the RF data of simulated viscoelastic materials subject to a VisR beamsequence with SNRs from 0-50dB in steps of 10dB. Tracked displacements were used as features for a neural network model to estimate elastic and viscous moduli. Predicted elastic moduli RMSE was smaller than the simulated material elastic step size down to 20dB SNR and viscous moduli RMSE was smaller than the viscous step size down to 40dB SNR. These findings suggest QVisR can be used to estimate viscoelastic moduli in noisy environments that more closely model non-simulation data acquisitions.

Acoustic Radiation Force Impulse (ARFI) Log Variance of Acceleration (log(VoA)) was previously demonstrated with a single ARFI excitation and plane wave (PW) tracking to detect plaque structure and composition. However, a separate multi-angle sequence was required to enable simultaneous display of 2-D vector-Doppler (VD) and plaque morphology. This paper evaluates the performance of various multi-angle compounded PW implementations of log(VOA). Experiments were conducted in a CIRS elastic phantom and in an excised human cadaveric carotid. In phantoms, increasing SNR by way of compounding increased inclusion SNR and CNR. Increasing the angle range over which PWs were steered decreased SNR, but did not appreciably change CNR. When applied to the ex vivo carotid, an angle range of 10° best predicted calcium area (as validated by spatially-aligned, expert-segmented histology), and higher number of angles spanning 10° improved delineation of plaque boundaries. These results suggest that, compared to an unsteered PW sequence, a multi-angle compounded PW sequence improves log(VoA) performance, suggesting the potential for extension to implementation with concurrent vector Doppler imaging.

Double-profile intersection (DoPIo) ultrasound combines two displacement profiles capturing identical tissue motion following an acoustic radiation force (ARF) push to estimate shear elastic modulus via an empirically derived model. However, the displacement-tracking scheme may be impacted by differences in focal configurations for both the push and track beams. A wider push beam imparts a more uniform displacement gradient than narrow ARF pushes for on-axis tracking beams, while the simultaneous formation of two displacement profiles from a single, wide transmit pulse enables the tracking of identical scatterer distributions at the cost of diminished differences between the two displacement profiles. In silico experiments suggested that DoPIo acquisitions performed using a wide ARF push beam and simultaneous tracking provided the most accurate and precise elasticity estimates. While all four combinations of parameters enabled the differentiation of a soft inclusion within a stiff background in vitro, elasticity estimates on a commercially calibrated phantom were consistently overestimated.

The kidney is an anisotropic organ, with higher elasticity along versus across nephrons. The degree of mechanical anisotropy in the kidney may be diagnostically relevant if properly exploited; however, if improperly controlled, anisotropy may confound stiffness measurements. The purpose of this study is to demonstrate the clinical feasibility of acoustic radiation force (ARF)-induced peak displacement (PD) measures for both exploiting and obviating mechanical anisotropy in the cortex of human kidney allografts, in vivo . Validation of the imaging methods is provided by preclinical studies in pig kidneys, in which ARF-induced PD values were significantly higher ( $p , Wilcoxon) when the transducer executing asymmetric ARF was oriented across versus along the nephrons. The ratio of these PD values obtained with the transducer oriented across versus along the nephrons strongly linearly correlated ( $R^{2} = 0.95$ ) to the ratio of shear moduli measured by shear wave elasticity imaging. On the contrary, when a symmetric ARF was implemented, no significant difference in PD was observed ( $p > 0.01$ ). Similar results were demonstrated in vivo in the kidney allografts of 14 patients. The symmetric ARF produced PD measures with no significant difference ( $p > 0.01$ ) between along versus across alignments, but the asymmetric ARF yielded PD ratios that remained constant over a six-month observation period post-transplantation, consistent with stable serum creatinine level and urine protein-to-creatinine ratio in the same patient population ( $p> 0.01$ ). The results of this pilot in vivo clinical study suggest the feasibility of 1) implementing symmetrical ARF to obviate mechanical anisotropy in the kidney cortex when anisotropy is a confounding factor and 2) implementing asymmetric ARF to exploit mechanical anisotropy when mechanical anisotropy is a potentially relevant biomarker.

While in vivo acoustic radiation force impulse (ARFI)-induced peak displacement (PD) has been demonstrated to have high sensitivity and specificity for differentiating soft from stiff plaque components in patients with carotid plaque, the parameter exhibits poorer performance for distinguishing between plaque features with similar stiffness. To improve discrimination of carotid plaque features relative to PD, we hypothesize that signal correlation and signal-to-noise ratio (SNR) can be combined, outright or via displacement variance. Plaque feature detection by displacement variance, evaluated as the decadic logarithm of the variance of acceleration and termed “log(VoA),” was compared to that achieved by exploiting SNR, cross correlation coefficient, and ARFI-induced PD outcome metrics. Parametric images were rendered for 25 patients undergoing carotid endarterectomy, with spatially matched histology confirming plaque composition and structure. On average, across all plaques, log(VoA) was the only outcome metric with values that statistically differed between regions of lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Further, log(VoA) achieved the highest contrast-to-noise ratio (CNR) for discriminating between LRNC and IPH, COL and CAL, and grouped soft (LRNC and IPH) and stiff (COL and CAL) plaque components. More specifically, relative to the previously demonstrated ARFI PD parameter, log(VoA) achieved 73% higher CNR between LRNC and IPH and 59% higher CNR between COL and CAL. These results suggest that log(VoA) enhances the differentiation of LRNC, IPH, COL, and CAL in human carotid plaques, in vivo , which is clinically relevant to improving stroke risk prediction and medical management.

Our group has previously found that in silico, mechanical anisotropy may be interrogated by exciting transversely isotropic materials with geometrically asymmetric acoustic radiation force excitations and then monitoring the associated induced displacements in the region of excitation. We now translate acoustic radiation force-based anisotropy assessment to human muscle in vivo and investigate its clinical relevance to monitoring muscle degeneration in Duchenne muscular dystrophy (DMD). Clinical anisotropy assessments were performed using Viscoelastic Response ultrasound, with a degree of anisotropy reflected by the ratios of Viscoelastic Response relative elasticity (RE) or relative viscosity (RV) measured with the asymmetric radiation force oriented parallel versus perpendicular to muscle fiber alignment. In vivo results from rectus femoris and gastrocnemius muscles of boys aged ∼7.9–10.4 y indicate that RE and RV anisotropy ratios in rectus femoris muscles of boys with DMD were significantly higher than those of healthy control boys (RE: DMD = 1.51 ± 0.87, control = 0.99 ± 0.69, p = 0.04, Wilcoxon rank sum test; RV: DMD = 1.04 ± 0.71, control = 0.74 ± 0.22, p = 0.02). In the gastrocnemius muscle, only the RV anisotropy ratio was significantly higher in dystrophic than control patients (DMD = 1.23 ± 0.35, control = 0.88 ± 0.31, p = 0.04). In the dystrophic rectus femoris muscle, the RE anisotropy ratio was inversely correlated (slope = –0.03/lbf, r = –0.43, p = 0.07, Pearson correlation) with quantitative muscle testing functional output measures but was not correlated with quantitative muscle testing in the dystrophic gastrocnemius. These results suggest that Viscoelastic Response RE and RV measures reflect differences in mechanical anisotropy associated with functional impairment with dystrophic degeneration that are relevant to monitoring DMD clinically.

Acoustic Radiation Force Impulse (ARFI) imaging employs sound impulses to induce and track tissue motion that conveys mechanical property. In application to carotid atherosclerotic plaque, ARFI noninvasively delineates features that convey risk for ischemic stroke: intraplaque hemorrhage, lipid-rich necrotic core, thin or ruptured fibrous cap, diffuse collagen, and calcium deposits. This talk will feature recent advances to in vivo ARFI atherosclerosis imaging technology, including the use of machine learning for automated feature classification, which are validated histologically in patients undergoing carotid endarterectomy. New developments, including incorporating vector blood flow estimation for combined shear strain assessment, will be highlighted.

Many biological tissues, including muscle or kidney, are mechanically anisotropic, and the degree of anisotropy (DoA) in mechanical properties is diagnostically relevant. DoA can be assessed either using the ratio of shear wave velocities (SWVs) or acoustic radio forced impulse (ARFI)-induced peak displacements (PD) measured longitudinal over transverse orientations. Whether using SWV or PD as a basis, DoA expressed as the ratio of values requires 90° transducer rotation when a linear array is employed. This large rotation angle is prone to misalignment errors. One solution is the use of a fully sampled matrix array for electronic rotation of point spread function (PSF). However, the challenges of matrix array are its high fabrication cost and complicated fabrication procedures. The cheaper and simpler alternative of matrix array is the use of a row–column array. A $3\times64$ elements 1.5-D array with a row–column excitation mode is proposed to assess DoA in mechanical properties using the PD ratio. Different numbers of elements in elevational and lateral directions were selected to have orthogonal ARFI excitation beams without rotating the transducer. A custom-designed flex circuit was used to fabricate the array with a simpler electrode connection than a fully sampled matrix array. The performance of the array was evaluated in Field II simulation and experiment. The output pressure was 0.57-MPa output under a 40- ${V}_{\text {pp}}$ excitation with a −6-dB point spread dimension of $14\times4$ mm2 in orthogonal directions. The PD was measured to be $1.4~\mu \text{m}$ in an isotropic elastic phantom with Young’s modulus of 5.4 kPa. These results suggest that the array is capable of assessing DoA using PD ratio without physical rotation of the transducer. The array has the potential to reduce the misalignment errors for DoA assessment.

Many tissues, such as muscle, kidney, and breast, are mechanically anisotropic. Appropriately exploited, mechanical anisotropy can be a clinically relevant target for noninvasive imaging. An imaging method's potential for interrogating mechanical anisotropy can be experimentally evaluated using tissue-mimicking materials, also known as phantoms. The objective of this work is to demonstrate the feasibility of constructing mechanically anisotropic phantoms using 3D-printed polylactic acid (PLA) fibers embedded in gelatin hydrogel or polyvinyl alcohol (PVA) cryogel. Four identical fiber sets were printed; two were embedded in gelatin and two in PVA. Acoustic Radiation Force Impulse (ARFI) imaging was performed on the constructed phantoms, with data acquisitions at 0°, 30°, 60°, and 90° concentric orientations, where 0° and 90° corresponded to the long-axis of the spatially asymmetric ARF excitation being aligned across and along the fibers, respectively. Degree of anisotropy (DoA) was calculated as the ratio of peak displacements achieved at 90° versus 0° orientations. While both gelatin and PVA embedded fibers demonstrated elastic anisotropy, DoA values were 32% higher in gelatin. These pilot experimental results demonstrate that phantoms constructed of 3D-printed PLA fibers embedded in gelatin or PVA exhibit mechanical anisotropy as assessed by ARFI ultrasound.

Viscoelastic Response (VisR) ultrasound is a noninvasive method for interrogating the viscoelastic properties of tissue by observing, in the region of excitation, tissue displacement in response to successive acoustic radiation force impulses. Recent VisR technology advancements now enable estimation of mechanical anisotropy and texture as well as, via machine learning approaches, quantitative evaluation of elastic and viscous moduli (as in Quantitative VisR (QVisR)). The diagnostic relevance of VisR outcome measures have been demonstrated for monitoring renal transplant status, differentiating malignant from benign breast masses, and delineating dystrophic muscle degeneration. For the latter two applications, method and pilot clinical results are reviewed herein. VisR-derived relative elasticity (RE) and relative viscosity (RV) mechanical anisotropy measures differentiated malignant from benign breast masses in women with sensitivities of 0.94 and specificities of 1.00, with accuracies of 0.96. In dystrophic muscle, QVisR-derived longitudinal shear elastic modulus in rectus femoris, vastus lateralus, sartorius, and gastrocnemius muscles statistically different between dystrophic and control for ages less than six years (Wilcoxon, p

While SWEI methods require shear wave propagation to be sampled across a measurement kernel, the recently-introduced double-profile intersection (DoPIo) ultrasound can estimate shear moduli locally and on-axis by exploiting ARF-induced displacement underestimation caused by scatterer shearing under a tracking PSF. We herein perform a systematic comparison of DoPIo to MTL/STL-SWEI to identify the benefits and limitations of using multiple tracking F-numbers. We further compare DoPIo performance achieved using conventional normalized cross-correlation (NCC) to that using a blind source separation (BSS) estimator by assessing its impact of mechanical feature resolution and measurements via automatic segmentation. DoPIo shear modulus estimates in a simulated elastic phantom using BSS resulted in a contrast transfer efficiency comparable to STL-SWEI (0.81 for a 12.0 kPa feature within an 8.0 kPa background using BSS-DoPIo, compared to 0.77 for NCC, 0.76 for MTL-SWEI, and 0.81 for STL-SWEI). We also demonstrate DoPIo measurements in a calibrated phantomm using a clinical ultrasound scanner system. In doing so, we present DoPIo as a method to ultrasonically measure the shear modulus of tissue that may be performed on-axis independently of ARF amplitudes.

Double-profile intersection (DoPIo) ultrasound is a new acoustic radiation force (ARF) imaging technique that exploits scatterer shearing under two different tracking point spread functions to estimate shear elastic moduli. Previous versions of DoPIo used an empirically derived model to relate the time where two displacement profiles, each measuring displacement in identical positions using different aperture sizes. We herein propose an alternative, machine learning-based approach that obviates the need for an empirically-derived model by replacing it with a support vector machine that uses paired displacement profiles in their entirety. In this work, we describe the implementation of such a model, validate its ability in shear modulus for simulated materials, and deploy it to image a calibrated phantom.

We present a quantitative extension of Viscoelastic Response (VisR) ultrasound that estimates shear elastic and viscous moduli from on-axis VisR displacement profiles in silico. Isotropic, homogeneous, linearly viscoelastic materials ranging from 1.57-33.33 kPa shear elasticity and 0.0033-2.34 Pa.s shear viscosity subject to a VisR beamsequence at 26 focal depths were simulated. Multi-target regression machine learning models were used to estimate shear elasticity and shear viscosity given the displacement profile, focal depth, and axial depth. The best performing models achieve a shear elasticity RMSE of 0.29 kPa and a shear viscosity RMSE of 0.13 Pa.s when predictions were made on the test set. These results suggest that machine learning methods can be used to quantitatively estimate viscoelasticity from VisR displacement profiles.

Vascular hemodynamics and plaque composition are better indicators of stroke risk than degrees of stenosis alone. In silico work previously indicated that the acoustic radiation force impulse (ARFI) derived parameter, variance of acceleration (VoA), may be implemented with plane wave (PW) acquisitions to offer a high frame rate method of differentiating plaque components. Vector flow imaging methods such as multi-angle plane wave vector Doppler further allow 2D blood flow tracking and assessments of wall shear stress (WSS) to better predict rupture potential. We hypothesize ARFI VoA and plane wave vector Doppler may be efficiently combined to interrogate plaque structure and composition and estimate WSS in human carotid arteries. While focused ARFI - VoA best predicts feature size compared to PW acquisitions, PW ARFI-VoA offers comparable resolution, but contrast of lower SNR features is reduced. The PW ARFI-VoA sequence may be supplemented with a steered PW sequence to additionally visualize blood flow and estimate wall shear stress at potential real time frame rates. This work demonstrates the feasibility of the technique to an ex-vivo left carotid from a recently deceased 63-year-old female.

Delineating carotid plaque components that confer rupture risk is vital to stoke prevention. We have previously shown that combining temporal profiles of Acoustic Radiation Force Impulse (ARFI)-induced displacement, cross-correlation coefficient, and signal-to-noise ratio (SNR) as the input feature set to a machine learning classifier enabled the classifier to differentiate intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), collagen (COL), and calcium (CAL) plaque components, with accurate fibrous cap thickness measurement. We hypothesize that machine learning-based classification of carotid plaque structure and composition will be improved by including more information in the input feature set. This study analyzed six carotid plaques imaged in vivo in patients undergoing carotid endarterectomy CEA). Data were acquired with ECG gating to diastole and to systole, with and without ARF excitation. From these data, temporal profiles of displacement, crosscorrelation coefficient (CC), and signal-to-noise ratio (SNR) were generated and used as inputs to a support vector machine classifier. The classifier was trained and tested using spatially matched histology. Over all examined plaques, combining acquisitions at systole and diastole, with and without ARFI push, achieved CNRs that were statistically higher than any one or combination of two inputs. These results suggest that the performance of a machine learning classifier for automatic plaque feature delineation can be improved by optimizing the feature set, with clinical relevance to in vivo human carotid plaque feature delineation herein demonstrated.

Differentiating malignant from benign breast lesions remains a challenge that may be met by a new imaging biomarker: lesion-to-background mechanical anisotropy assessed by VisR ultrasound. VisR is an ultrasound imaging method that uses 2 consecutive acoustic radiation force excitations, separated in time and delivered to the same region of excitation, to directionally interrogate peak displacement (PD), relative elasticity (RE), and relative viscosity (RV). From these directional measurements, mechanical degree of anisotropy is measured. In a pilot clinical study involving 29 women with BIRADS-4 or −5 breast masses, VisR PD, RE, and RV were measured in lesions and in surrounding tissue at 0°, 30°, 60°, and 90° concentric orientations. From these measurements, lesion DoA (LDoA), surrounding tissue DoA (SDoA), and log(LDoA/SDoA) were assessed per parameter and compared between biopsy-confirmed malignant versus benign masses. Across all patients, PD-, RE-, and RV-based LDoA or SDoA alone did not achieve statistical difference between malignant and benign masses (Wilcoxon, $\mathrm{p} > 0.05$ ). However, PD-, RE-, and RV-based log(LDoA/SDoA) statistically differentiated malignant from benign masses (Wilcoxon, $\mathrm{p} ), with AUC values of 0.96 for PD-based, and 0.94 for RE- and RV-based outcomes. These results demonstrate the relevance of lesion-to-background mechanical anisotropy assessed by VisR ultrasound for differentiating malignant from benign breast masses in women, in vivo.

Stroke is often caused by carotid plaque rupture, with rupture potential indicated by plaque structure and composition. Delineating carotid plaque components that confer rupture risk is vital to stoke prevention. We have previously shown that ARFI Log Variance of Acceleration, log(VoA), discriminates intraplaque hemorrhage (IPH) from lipid-rich necrotic core (LRNC) and collagen (COL) from calcium (CAL) in vivo in humans, while ARFI peak displacement does not. We hypothesize that log(VoA) performance will be further improved by harmonic tracking of ARF-induced displacement. In a pilot clinical study of six carotid plaques imaged in vivo in patients undergoing carotid endarterectomy (CEA), plaque component contrast-to-noise ratio (CNR) achieved by harmonic log(VoA) was compared to that achieved by tracking at fundamental low (4.0 MHz, FL) and high (8.89 MHz, FH) frequencies with validation by spatially-matched histology derived from the extracted plaque specimens. Over all examined plaques, harmonic and both fundamental low and high log(VoA) distributions statistically differentiated plaque components (Wilcoxon, $\mathrm{p} , panel iv). Harmonic log(VoA) achieved the highest CNR between COL and LRNC, which is relevant to fibrous cap delineation, but FH tracking yielded the highest CNR between LRNC and IPH and between COL and CAL. These results suggest that combined harmonic and FH tracking could improve ARFI log(VoA) characterization of carotid plaque components that are correlated to rupture.

ARFI Variance of Acceleration (VoA) has been previously used in clinical applications to delineate atherosclerotic plaque morphology and detect subcutaneous hemorrhage. In silico models also demonstrated that behavior of the parameter is influenced by error in the displacement estimates, which are accentuated by decreasing SNR or correlation. This study experimentally demonstrates that in non-elastic materials, decreasing correlation, induced by a greater magnitude ARFI push, increases VoA when the material SNR is above 40–45 dB. This signal correlation dependent change is realizable with focused and PW tracking, potentially expanding the clinical utility of high framerate VoA analysis.

Viscoelastic response (VisR) ultrasound characterizes the viscoelastic properties of tissue by fitting acoustic radiation force (ARF)-induced displacements in the region of ARF excitation to a 1-D mass-spring-damper (MSD) model. Elasticity and viscosity are calculated separately but relative to the applied ARF amplitude. We refer to these parameters as “relative elasticity (RE)” and “relative viscosity (RV).” We herein test the hypothesis that RE and RV linearly correlate to true elasticity and viscosity in tissue. VisR imaging was simulated in 144 homogeneous viscoelastic materials with varying elasticities and viscosities. Derived RE linearly correlated with material elasticity and varied by an average of 2.52% when the material viscosity changed from 0.1 to 1.3 Pa $\cdot $ s. Derived RV linearly correlated with material viscosity but varied by an average of 102.5% when material elasticity changed from 3.33 to 20 kPa. The effect of elasticity on RV measurement was compensated using the slope of the linear relationship between RV and natural frequency ( $\omega _{text{n}}$ ). After compensation, RV $^{\text {EC}}$ (elasticity compensated RV) linearly correlated with material viscosity and varied by less than 1.00% on average when the modeled shear elastic modulus changed from 3.3 to 20 kPa. In addition to elasticity compensation, variation in ARF amplitude over depth was compensated, yielding REDC and ${\text {RV}}_{\text {DC}}^{\text {EC}}$ . REDC and ${\text {RV}}_{\text {DC}}^{\text {EC}} $ successfully contrasted elastic and viscous inclusions, respectively, in three simulated phantoms. Experimentally, in the homogeneous oil-in-gelatin phantoms and excised livers, REDC linearly correlated with shear wave dispersion ultrasound vibrometry (SDUV) derived shear elastic modulus, and ${\text {RV}}_{\text {DC}}^{\text {EC}}$ linearly correlated with SDUV-derived shear viscosity. In excised livers containing viscoelastic oil-in-gelatin inclusions, the inclusions were successfully contrasted from the liver background by both REDC and ${\text {RV}}_{\text {DC}}^{\text {EC}}$ . These results suggest that RE and RV are relevant for qualitatively assessing the elastic and viscous properties of tissue.

Direct ultrasonic imaging of arterial and venous thrombi could aid in diagnosis and treatment planning by providing rapid and cost-effective measurements of thrombus volume and elastic modulus. Toward this end, it was demonstrated that open-air magnetomotive ultrasound (MMUS) provides specific contrast to superparamagnetic iron oxide-labeled model thrombi embedded in gelatin-based blood vessel-mimicking flow phantoms. MMUS was performed on model thrombi in the presence of pulsatile flow that mimics cardiac-induced motion found in real vasculature. The MMUS signal and contrast-to-noise ratio (CNR) were measured across a range of physiologically relevant thrombus volumes and elastic moduli. Model thrombus volumes as small as 0.5 ml were shown to be detectable (CNR > 1) over the entire range of elastic moduli tested (3.5–40 kPa). It was also found that MMUS signal and CNR are increased with increasing thrombus volume ( $r = 0.99$ ) and decreasing elastic modulus ( $r = -0.81$ ), while variations in pulsatile flow rate had little effect. These findings demonstrate that MMUS has promise as a direct in vivo thrombosis imaging modality for quantifying thrombus volume and stiffness.

Chronic kidney disease is most desirably and cost-effectively treated by renal transplantation, but graft survival is a major challenge. Although irreversible graft damage can be averted by timely treatment, intervention is delayed when early graft dysfunction goes undetected by standard clinical metrics. A more sensitive and specific parameter for delineating graft health could be the viscoelastic properties of the renal parenchyma, which are interrogated non-invasively by Viscoelastic Response (VisR) ultrasound, a new acoustic radiation force (ARF)-based imaging method. Assessing the performance of VisR imaging in delineating histologically confirmed renal transplant pathologies in vivo is the purpose of the study described here. VisR imaging was performed in patients with (n = 19) and without (n = 25) clinical indication for renal allograft biopsy. The median values of VisR outcome metrics (τ, relative elasticity [RE] and relative viscosity [RV]) were calculated in five regions of interest that were manually delineated in the parenchyma (outer, center and inner) and in the pelvis (outer and inner). The ratios of a given VisR metric for all possible region-of-interest combinations were calculated, and the corresponding ratios were statistically compared between biopsied patients subdivided by diagnostic categories versus non-biopsied, control allografts using the two-sample Wilcoxon test (p 0.05). Although τ ratios non-specifically differentiated allografts with vascular disease, tubular/interstitial scarring, chronic allograft nephropathy and glomerulonephritis from non-biopsied control allografts, RE distinguished only allografts with vascular disease and tubular/interstitial scarring, and RV distinguished only vascular disease. These results suggest that allografts with scarring and vascular disease can be identified using non-invasive VisR RE and RV metrics.

Degree of mechanical anisotropy in renal cortex can be assessed by evaluating the ratio of viscoelastic response (VisR) parameters achieved when the long axis of a spatially asymmetric ARF excitation PSF is aligned along versus across nephrons. In this work, intra-observer reproducibility and variability in VisR derived DoA assessment in renal cortex due to donor type, sex, race, and BMI is evaluated in healthy human kidney allografts, in vivo. Transcutaneous, in vivo VisR imaging was performed in 20 kidney allografts 2 months post-transplantation using a Siemens Acuson Antares™ imaging system and a VF7-3 linear array transducer. Ratios of VisR derived relative elasticity (RE), relative viscosity (RV), and peak displacement (PD) obtained with the ARF short-axis aligned along versus across nephrons were calculated. Patients were divided based on their sex (male: N=14 versus female: N=6), race (African American: N = 6 versus Caucasian: N=14), donor type (living: N=14 versus deceased: N =6), and BMI (BMI >30: N= 8 versus BMI 0.05) due to sex, race, donor types, or BMI. The median absolute percent difference in (PD, RE, RV) ratios were (23, 13, 23) due to sex, (5, 3, 3) due to race, (0.35, 14, 13) due to donor type, and 8.6, 6, 12) due to BMI. Ratios of PD, RE, and RV were not statistically different between 1st and 2nd imaging events with mean percent difference in (PD, RE, and RV) ratios of (-0.04, -0.11, and - 0.01). These findings suggest that VisR outcome measures are robust to potentially confounding demographic factors and reproducible by a trained practitioner.

Low blood SNR makes detecting small vessels, measuring slow flow rates, and assessing blood perfusion without contrast administration challenging. A potential approach is to use Variance of Acceleration (VoA) imaging, which has been demonstrated previously in humans in vivo for monitoring subcutaneous bleeding and delineating intraplaque hemorrhage. We hypothesize that, without the addition of contrast agents, VoA can detect slow flow in small vessels for perfusion assessment. Experiments were performed on an ATS 700-D 527 calibrated flow phantom (vessel diameter of 1 mm, flow rates of 0-53 cm/s) and in the surgically exteriorized right kidneys of 3 pigs (2M/1F, mean body weight of 74.4 ± 9.3 kg) at baseline and after inducing ischemia. In the phantom, logVoA increased in the vessel with increasing flow rate, and it remained constant in the background. LogVoA values were higher in the background with versus without the ARF excitation. Similarly, logVoA values were higher in the vessel, particularly for flow rates < 12 cm/s, with versus without ARF excitation. Vessel CNR by logVoA was higher with versus without ARF excitation for flow rates < 12 cm/s, and CNR by logVoA was greater than CNR by power Doppler for all flow rates. Additionally, logVoA was statistically significantly greater at baseline than ischemia in all three in vivo pig kidneys. Finally, logVoA delineates a 1.25 mm-diameter vessel in a pig renal cortex in vivo, while power Doppler does not. These results suggest that logVoA could support contrast-free detection of slow blood flow in small vessels and in vivo perfusion assessment.

A significant risk factor in ischemic strokes is carotid atherosclerotic plaque that is susceptible to rupture, with rupture potential conveyed by plaque composition and structure. Prior work has shown that Variance of Acceleration (VOA), derived from Acoustic Radiation Force Impulse (ARFI) imaging, is capable of delineating plaque components. However, the method relies on focused ARF excitations at each lateral location, limiting framerate. To increase framerate, it is hypothesized that VoA imaging may be implemented using fast plane wave imaging techniques. In this work, VoA’s ability to discriminate materials based on signal decorrelation and SNR is compared for conventional focused wave versus plane wave tracking methods. Relative to focused wave tracking, plane wave tracking yields comparable discrimination. This study suggests that a plane wave implementation of ARFI VoA analysis could be feasible for increased frame rate in atherosclerosis imaging.

Tissue elasticity can be measured by applying an acoustic radiation force (ARF) excitation and tracking mechanical responses like deformation or shear wave propagation via displacement tracking. However, tissue displacements tracked by ultrasound is underestimated due to the shearing of scatterers under the tracking point-spread function. While such displacement underestimation is a source of error in conventional elasticity approaches, we herein present an alternative method that exploits displacement underestimation: double-profile intersection (DoPIo) elastography. We hypothesize that DoPIo can quantify material elasticity in silico.

Mechanical property changes associated with inflammation, necrosis, fibrosis, and fatty deposition in dystrophic muscle may be quantitatively evaluated by Quantitative Viscoelastic Response (QVisR) ultrasound. QVisR uses a machine learning (ML) framework that takes as input tissue displacement in response to two consecutive and co-located acoustic radiation force (ARF) excitations and yields as output estimates of shear elastic and shear viscous moduli. QVisR imaging was performed in the vastus lateralis (VL) muscles of 11 boys with Duchenne muscular dystrophy (DMD) aged 5 to 12 years and of 8 age-matched boys with no known neuromuscular disorders, who served as controls. QVisR measures of elastic moduli differed between DMD and control VL in boys aged less than six years and six-to-seven years. Similarly, QVisR measures of viscous moduli differed between DMD and control VL in boys aged six-to-seven years. These results demonstrate that QVisR measures of elastic and viscous moduli differentiate dystrophic from control muscle. The findings suggest that QVisR may be relevant to monitoring dystrophic muscle degeneration and response to intervention, particularly in early stages when interventions are most likely to be impactful.

Chronic kidney disease is associated with progressive inflammation and fibrosis, which is conventionally assessed by invasive biopsy. A noninvasive alternative to biopsy may be interrogating elastic and viscous anisotropy. This work evaluates the feasibility of using in vivo Viscoelastic Response (VisR) ultrasound for detecting changes in the elastic and viscous degree of anisotropy (DoA) associated with fibrosis after chronic ischemia-reperfusion injury (IRI) in pig kidney. Histology was used to validate VisR DoA measurements, which were compared to those derived using Shear Wave Elasticity Imaging (SWEI) and Shearwave Dispersion Ultrasound Vibrometry (SDUV). In the injured and contralateral (control) kidneys of 8 pigs, VisR, SWEI, and SDUV-derived elastic DoA, evaluated as the ratio of longitudinal over transverse shear elasticity, was significantly (p

Breast cancer screening is beneficial when it averts progression of disease to metastasis, but adverse effects to patients and unnecessary medical expense may result downstream from false positives. Achieving early breast cancer detection with high sensitivity and specificity remains a challenge that may be met by using acoustic radiation force to assess the mechanical properties of tissue. The objective of this study is to evaluate in vivo the diagnostic relevance of Viscoelastic Response (VisR)-derived metrics for mechanical anisotropy. We compare our in vivo human results against biopsy findings. This study analyzed 9 breast lesions (4 malignant, 5 benign) imaged in vivo in women with BIRADS-4 or -5 ratings after standard screening. VisR relative elasticity (RE), relative viscosity (RV), and peak displacement (PD) were measured for each transducer orientation, and fit to a sinusoid by least-squares minimization, extrapolating to 360°. The fits were phase-aligned, and the point-wise differences in each parameter between lesion and background were integrated over angle. Finally, the ratio of the maximum to the minimum parameter value was calculated to reflect the degree of anisotropy (DoA). DoAs by PD, RE, and RV were statistically significantly greater in background than in lesion for all malignant cases but statistically significantly smaller in background than in lesion for all benign cases (Wilcoxon, p < 0.05). Additionally, differences between lesion and background integrated over angle by RE, RV and PD were statistically significantly different (Wilcoxon, p < 0.05) for malignant and benign lesions across all examined patients. These results suggest that VisR-derived mechanical anisotropy assessment could be diagnostically relevant for discriminating malignant from benign breast lesions.

Quantitative Viscoelastic Response (QVisR) ultrasound is a new machine learning-based elasticity imaging method in which elastic and viscous moduli are estimated from tissue deformation in response to two consecutive acoustic radiation force (ARF) excitations. The estimated moduli are rendered into two-dimensional parametric images of elastic and viscous modulus. From QVisR images, the spatial distribution of elastic and viscous properties may be evaluated using established computational texture analysis tools. In this study, computational texture analysis by the omnidirectional gray-level run-length matrix (GLRLM) was applied to QVisR images. The images were obtained in the vastus lateralis (VL) muscles of 11 boys with Duchenne muscular dystrophy, aged 5-12 years, and 8 age-matched boys with no known neuromuscular disorders, who served as controls. GLRLM-derived elastic entropy in QVisR images was statistically higher (Wilcoxin, p

Mechanical property assessment by elastographic ultrasound methods, including those that exploit acoustic radiation force (ARF), relies on accurate estimation of tissue displacement. Several displacement estimators have been developed, but their relevance to tracking ARF-induced displacements smaller than one micrometer is limited by estimation variance. To minimize displacement estimation variance, a new blind source separation (BSS)-based approach that exploits the spatial distribution of displacements is presented. This new approach applies principal component analysis (PCA) in three-dimensional (3D) kernels to derive dominant eigenvectors, from which displacements are estimated. We call this new approach the ‘3DK-BSS’ estimator. The 3DK-BSS estimator is evaluated in terms of contrast-to-noise ratio (CNR) achieved in ARFI peak displacement (PD) images of a stiff (80 kPa) and a soft (8 kPa) spherical inclusion in a commercial phantom with a 25 kPA background. The CNR values achieved by 3DK-BSS were compared to those produced by normalized cross-correlation (NCC), Bayesian regularization, and BSS implemented using a two-dimensional (2D) kernel at ARF power levels of 5 to 45% of the full system power. For all power levels, and for both stiff and soft inclusions, 3DK-BSS inclusion CNR was higher than any other examined displacement estimator. For example, 3DK-BSS stiff inclusion CNR was 16.3, 11.8, and 4.2 times higher than the CNRs achieved by NCC, Bayesian regularization, and 2D BSS, respectively at 5% power level. CNR improvement by 3DK-BSS was largest at the lowest (5%) ARF power level, where displacement in the stiff phantom was measured by 3DK-BSS as 250 nm. These results suggest that, by accurately measuring sub-micrometer displacements, the 3DK-BSS displacement estimator could enable deeper ARF-based mechanical property assessments, finer mechanical resolution in stiff tissues, and/or lower ARF power requirements to expand the diagnostic relevance of ARF-based mechanical property assessments.

Atherosclerotic plaque rupture potential is conferred by plaque composition and structure. We have previously shown in humans in vivo that carotid plaque components can be automatically delineated by a support vector machine (SVM) classifier considering normalized crosscorrelation (NCC)-derived measures of ARFI-induced displacement. We now extend our prior work by hypothesizing that classification is improved by using displacements derived using blind source separation (BSS). In 20 carotid plaques imaged in vivo in patients undergoing carotid endarterectomy (CEA) were imaged prior to extraction, and specimens were harvested after CEA for histological processing. ARFI displacement profiles were calculated from each of the first five principal components of the RF data and used as inputs to the SVM classifier. The classifier was evaluated by 5-fold cross-validation, with the histological samples acting as gold standards. From the output SVM likelihood matrices, ROC curves were calculated for separating collagen from calcium and lipid-rich necrotic core from intraplaque hemorrhage. For all examined plaques, inputting displacement profiles derived from the first four eigenvectors to the SVM classifier increased sensitivity and specificity over using NCCderived displacement profiles. These results suggest that using BSS-derived displacement profiles as inputs to the SVM classifier improves discrimination of carotid plaque components that are correlated to vulnerability for rupture.