@article{behler_czernuszewicz_wu_nichols_zhu_homeister_merricks_gallippi_2013, title={Acoustic Radiation Force Beam Sequence Performance for Detection and Material Characterization of Atherosclerotic Plaques: Preclinical, Ex Vivo Results}, volume={60}, ISSN={["1525-8955"]}, DOI={10.1109/tuffc.2013.2847}, abstractNote={This work presents preclinical data demonstrating performance of acoustic radiation force (ARF)-based elasticity imaging with five different beam sequences for atherosclerotic plaque detection and material characterization. Twelve trained, blinded readers evaluated parametric images taken ex vivo under simulated in vivo conditions of 22 porcine femoral arterial segments. Receiver operating characteristic (ROC) curve analysis was carried out to quantify reader performance using spatially-matched immunohistochemistry for validation. The beam sequences employed had high sensitivity (sens) and specificity (spec) for detecting Type III+ plaques (sens: 85%, spec: 79%), lipid pools (sens: 80%, spec: 86%), fibrous caps (sens: 86%, spec: 82%), calcium (sens: 96%, spec: 85%), collagen (sens: 78%, spec: 77%), and disrupted internal elastic lamina (sens: 92%, spec: 75%). 1:1 single-receive tracking yielded the highest median areas under the ROC curve (AUC), but was not statistically significantly higher than 4:1 parallel-receive tracking. Excitation focal configuration did not result in statistically different AUCs. Overall, these results suggest ARF-based imaging is relevant to detecting and characterizing plaques and support its use for diagnosing and monitoring atherosclerosis.}, number={12}, journal={IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL}, author={Behler, Russell H. and Czernuszewicz, Tomasz J. and Wu, Chih-Da and Nichols, Timothy C. and Zhu, Hongtu and Homeister, Jonathon W. and Merricks, Elizabeth P. and Gallippi, Caterina M.}, year={2013}, month={Dec}, pages={2471–2487} }
 @article{behler_nichols_zhu_merricks_gallippi_2009, title={ARFI IMAGING FOR NONINVASIVE MATERIAL CHARACTERIZATION OF ATHEROSCLEROSIS PART II: TOWARD IN VIVO CHARACTERIZATION}, volume={35}, ISSN={["1879-291X"]}, DOI={10.1016/j.ultrasmedbio.2008.08.015}, abstractNote={Seventy percent of cardiovascular disease (CVD) deaths are attributed to atherosclerosis. Despite their clinical significance, nonstenotic atherosclerotic plaques are not effectively detected by conventional atherosclerosis imaging methods. Moreover, conventional imaging methods are insufficient for describing plaque composition, which is relevant to cardiovascular risk assessment. Atherosclerosis imaging technologies capable of improving plaque detection and stratifying cardiovascular risk are needed. Acoustic radiation force impulse (ARFI) ultrasound, a novel imaging method for noninvasively differentiating the mechanical properties of tissue, is demonstrated for in vivo detection of nonstenotic plaques and plaque material assessment in this pilot investigation. In vivo ARFI imaging was performed on four iliac arteries: (1) of a normocholesterolemic pig with no atherosclerosis as a control, (2) of a familial hypercholesterolemic pig with diffuse atherosclerosis, (3) of a normocholesterolemic pig fed a high-fat diet with early atherosclerotic plaques and (4) of a familial hypercholesterolemic pig with diffuse atherosclerosis and a small, minimally occlusive plaque. ARFI results were compared with spatially matched immunohistochemistry, showing correlations between elastin and collagen content and ARFI-derived peak displacement and recovery time parameters. Faster recoveries from ARFI-induced peak displacements and smaller peak displacements were observed in areas of higher elastin and collagen content. Importantly, spatial correlations between tissue content and ARFI results were consistent and observable in large and highly evolved as well as small plaques. ARFI imaging successfully distinguished nonstenotic plaques, while conventional B-mode ultrasound did not. This work validates the potential relevance of ARFI imaging as a noninvasive imaging technology for in vivo detection and material assessment of atherosclerotic plaques. (E-mail: [email protected])}, number={2}, journal={ULTRASOUND IN MEDICINE AND BIOLOGY}, author={Behler, Russell H. and Nichols, Timothy C. and Zhu, Hongtu and Merricks, Elizabeth P. and Gallippi, Caterina M.}, year={2009}, month={Feb}, pages={278–295} }
 @article{mauldin_zhu_behler_nichols_gallippi_2008, title={Robust principal component analysis and clustering methods for automated classification of tissue response to ARFI excitation}, volume={34}, ISSN={["1879-291X"]}, DOI={10.1016/j.ultrasmedbio.2007.07.019}, abstractNote={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.}, number={2}, journal={ULTRASOUND IN MEDICINE AND BIOLOGY}, author={Mauldin, F. William, Jr. and Zhu, Hongtu T. and Behler, Russell H. and Nichols, Timothy C. and Gallippi, Caterina M.}, year={2008}, month={Feb}, pages={309–325} }