@article{anand_torres_homeister_caughey_gallippi_2023, title={Comparing Focused-Tracked and Plane Wave-Tracked ARFI Log(VoA) In Silico and in Application to Human Carotid Atherosclerotic Plaque, Ex Vivo}, volume={70}, ISSN={["1525-8955"]}, DOI={10.1109/TUFFC.2023.3278495}, abstractNote={A significant risk factor for ischemic stroke is carotid atherosclerotic plaque that is susceptible to rupture, with rupture potential conveyed by plaque morphology. Human carotid plaque composition and structure have been delineated noninvasively and in vivo by evaluating log(VoA), a parameter derived as the decadic log of the second time derivative of displacement induced by an acoustic radiation force impulse (ARFI). In prior work, ARFI-induced displacement was measured using conventional focused tracking; however, this requires a long data acquisition period, thereby reducing framerate. We herein evaluate if ARFI log(VoA) framerate can be increased without a reduction in plaque imaging performance using plane wave tracking instead. In silico, both focused- and plane wave-tracked log(VoA) decreased with increasing echobrightness, quantified as signal-to-noise ratio (SNR), but did not vary with material elasticity for SNRs below 40 dB. For SNRs of 40–60 dB, both focused- and plane wave-tracked log(VoA) varied with SNR and material elasticity. Above 60 dB SNR, both focused- and plane wave-tracked log(VoA) varied with material elasticity alone. This suggests that log(VoA) discriminates features according to a combination of their echobrightness and mechanical property. Further, while both focused- and plane-wave tracked log(VoA) values were artifactually inflated by mechanical reflections at inclusion boundaries, plane wave-tracked log(VoA) was more strongly impacted by off-axis scattering. Applied to three excised human cadaveric carotid plaques with spatially aligned histological validation, both log(VoA) methods detected regions of lipid, collagen, and calcium (CAL) deposits. These findings support that plane wave tracking performs comparably to focused tracking for log(VoA) imaging and that plane wave-tracked log(VoA) is a viable approach to discriminating clinically relevant atherosclerotic plaque features at a 30-fold higher framerate than by focused tracking.}, number={7}, journal={IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL}, author={Anand, Keerthi S. and Torres, Gabriela and Homeister, Jonathon W. and Caughey, Melissa C. and Gallippi, Caterina M.}, year={2023}, month={Jul}, pages={636–652} }
 @article{anand_gallippi_2022, title={4D Cardiac Gated Vector Flow Imaging Accurately Measures WSS in a Pressurized Closed-Loop System}, ISSN={["1948-5719"]}, DOI={10.1109/IUS54386.2022.9958037}, abstractNote={Wall shear stress plays a critical role in atherosclerotic plaque remodeling and risk of rupture. High framerate volumetric imaging is required to capture potentially malignant hemodynamic forces on the plaque. We show that accurate volumetric wall shear stress estimation over time is feasible by sweeping a linear array transmitting plane wave vector Doppler sequences, with gated acquisitions. Pulsatile flow in a pressurized straight tube CIRS peripheral vascular phantom generated mimicked “end diastolic” and “peak systolic” wall shear stresses of 0.26 Pa and 1.63 Pa, respectively. The 4D vector flow imaging overall tracked changes in WSS along the tube with <15% error relative to the analytical ground truth, with slightly more underestimation near the sides of the tube. Together, the results demonstrate that, despite the poor elevational resolution of a linear array, volumetric WSS can be measured throughout the cardiac cycle. This approach may be relevant to volumetric evaluation of WSS in human carotid arteries, with extension to incorporating volumetric interrogation of plaque composition and structure.}, journal={2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)}, author={Anand, Keerthi S. and Gallippi, Caterina M.}, year={2022} }
 @article{anand_kolahdouz_homeister_smith_griffith_gallippi_2022, title={Concurrent ARFI Plaque Imaging and Wall Shear Stress Measurement in Human Carotid Artery, with Validation by Fluid Structure Interaction Models}, ISSN={["1948-5719"]}, DOI={10.1109/IUS54386.2022.9958828}, abstractNote={The rupture potential of an atherosclerotic plaque is dependent on both the plaque's composition and the shear stresses it encounters from blood flow. Because plaques move and deform throughout the cardiac cycle, resulting in changes to plaque position and shape as well as to the encountered shear stresses, concurrent imaging of both risk factors over time is required to accurately predict plaque vulnerability. To evaluate the potential to achieve as much, multi-angle plane wave (PW) ARFI and least-squares vector Doppler data were acquired in a calibrated flow phantom with channels of 4–8 mm diameters and flow rates of 100–600 ml/min. The wall shear stress (WSS) was measured to within 15% of the ground-truth analytical solutions. The same methods were then implemented in an excised human cadaveric carotid with a x% stenotic plaque. ARFI VoA detected plaque regions of calcium and intraplaque hemorrhage that were validated by spatially-matched histology. Concurrent vector Doppler yielded a peak WSS of 5.2 Pa on the plaque shoulder, which was consistent with the 6.4 Pa WSS predicted by an immersed interface fluid-solid interation (FSI) model developed using the specific geometry of the examined cadaveric carotid. Overall our results demonstrate the feasibility of concurrent imaging of carotid plaque composition by ARFI VoA, vector flow, and WSS to better assess stroke risk.}, journal={2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)}, author={Anand, Keerthi S. and Kolahdouz, Ebrahim M. and Homeister, Jonathon and Smith, Margaret-Anne and Griffith, Boyce E. and Gallippi, Caterina M.}, year={2022} }
 @article{czernuszewicz_aji_moore_montgomery_velasco_torres_anand_johnson_deal_zuki_et al._2022, title={Development of a Robotic Shear Wave Elastography System for Noninvasive Staging of Liver Disease in Murine Models}, ISSN={["2471-254X"]}, DOI={10.1002/hep4.1912}, abstractNote={Shear wave elastography (SWE) is an ultrasound‐based stiffness quantification technology that is used for noninvasive liver fibrosis assessment. However, despite widescale clinical adoption, SWE is largely unused by preclinical researchers and drug developers for studies of liver disease progression in small animal models due to significant experimental, technical, and reproducibility challenges. Therefore, the aim of this work was to develop a tool designed specifically for assessing liver stiffness and echogenicity in small animals to better enable longitudinal preclinical studies. A high‐frequency linear array transducer (12‐24 MHz) was integrated into a robotic small animal ultrasound system (Vega; SonoVol, Inc., Durham, NC) to perform liver stiffness and echogenicity measurements in three dimensions. The instrument was validated with tissue‐mimicking phantoms and a mouse model of nonalcoholic steatohepatitis. Female C57BL/6J mice (n = 40) were placed on choline‐deficient, L‐amino acid‐defined, high‐fat diet and imaged longitudinally for 15 weeks. A subset was sacrificed after each imaging timepoint (n = 5) for histological validation, and analyses of receiver operating characteristic (ROC) curves were performed. Results demonstrated that robotic measurements of echogenicity and stiffness were most strongly correlated with macrovesicular steatosis (R2 = 0.891) and fibrosis (R2 = 0.839), respectively. For diagnostic classification of fibrosis (Ishak score), areas under ROC (AUROCs) curves were 0.969 for ≥Ishak1, 0.984 for ≥Ishak2, 0.980 for ≥Ishak3, and 0.969 for ≥Ishak4. For classification of macrovesicular steatosis (S‐score), AUROCs were 1.00 for ≥S2 and 0.997 for ≥S3. Average scanning and analysis time was <5 minutes/liver. Conclusion: Robotic SWE in small animals is feasible and sensitive to small changes in liver disease state, facilitating in vivo staging of rodent liver disease with minimal sonographic expertise.}, journal={HEPATOLOGY COMMUNICATIONS}, author={Czernuszewicz, Tomasz J. and Aji, Adam M. and Moore, Christopher J. and Montgomery, Stephanie A. and Velasco, Brian and Torres, Gabriela and Anand, Keerthi S. and Johnson, Kennita A. and Deal, Allison M. and Zuki, Dzenan and et al.}, year={2022}, month={Feb} }
 @article{yokoyama_anand_gallippi_2021, title={Assessing the Impact of ARF Excitation Beam Width and Tracking Beam Concurrency on DoPIo Imaging Performance in a Calibrated Phantom}, ISSN={["1948-5719"]}, DOI={10.1109/IUS52206.2021.9593658}, abstractNote={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.}, journal={INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021)}, author={Yokoyama, Keita A. and Anand, Keerthi S. and Gallippi, Caterina M.}, year={2021} }
 @article{torres_caughey_anand_huang_lee_zamora_hung_merricks_ezzell_homeister_et al._2021, title={Atherosclerotic Plaque Characterization in Humans with ARFI Variance of Acceleration: Blinded Reader Study}, ISSN={["1948-5719"]}, DOI={10.1109/IUS52206.2021.9593630}, abstractNote={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.}, journal={INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021)}, author={Torres, Gabriela and Caughey, Melissa C. and Anand, Keerthi and Huang, Benjamin Y. and Lee, Ellie R. and Zamora, Carlos A. and Hung, Sheng-Che and Merricks, Elizabeth and Ezzell, J. Ashley and Homeister, Jonathon W. and et al.}, year={2021} }
 @article{torres_caughey_anand_homeister_farber_gallippi_2021, title={Automatic Classification of Human Carotid Plaque Features, In Vivo, Using Multiple Forms of ARFI Data}, ISSN={["1948-5719"]}, DOI={10.1109/IUS52206.2021.9593467}, abstractNote={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.}, journal={INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021)}, author={Torres, Gabriela and Caughey, Melissa C. and Anand, Keerthi and Homeister, Jonathon W. and Farber, Mark A. and Gallippi, Caterina M.}, year={2021} }
 @article{anand_gallippi_2021, title={Multiangle PW Compounding Supports ARFI Variance of Acceleration (VoA) Carotid Plaque Imaging for Integration with Vector Doppler}, ISSN={["1948-5719"]}, DOI={10.1109/IUS52206.2021.9593578}, abstractNote={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.}, journal={INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021)}, author={Anand, Keerthi S. and Gallippi, Caterina M.}, year={2021} }