ARFI Ultrasound for the Detection and Characterization of Atherosclerosis in an FH Pig Model

Stroke is the third leading cause of death in the United States, with a large percentage of strokes caused by atherosclerotic rupture. Current methods of atherosclerotic detection include invasive techniques such as coronary angiography and intravascular ultrasound (IVUS), as well as noninvasive techniques such as magnetic resonance angiography and duplex ultrasound. These methods are known to be effective for detecting occlusive plaques associated with pronounced narrowing of the vessel lumen and/or blood flow obstruction. However, they are not effective for detecting nonstenotic plaques or for characterizing plaque composition. This lack of plaque compositional information prevents these imaging techniques from detecting plaque rupture risk. To accurately assess atherosclerotic plaques most vulnerable to rupture, novel detection and characterization techniques are needed. Acoustic radiation force impulse (ARFI) ultrasound, one of several elastographic techniques under development to meet this need, uses high intensity acoustic impulses to remotely displace tissue. By assessing ARFI-induced displacement and subsequent tissue recovery, the mechanical properties of tissue can be assessed and used to characterize atherosclerosis. In order to ensure the best possible plaque detection capability, the most appropriate beam sequences must be used. Following ex vivo and in vivo demonstration of ARFI capability for atherosclerotic plaque detection and characterization, a statistical reader study of ARFI beam sequences is performed in phantoms as well as ex vivo and in vivo in an FH pig model. Finally, a serial study of ARFI is performed to assess ARFI repeatability and potential for early plaque detection. This dissertation supports the hypothesis: in vivo, transcutaneous ARFI ultrasound will detect occlusive and nonocclusive plaques in peripheral arteries, assess plaque composition and structure and detect changes in atherosclerotic status over time

ARFI Ultrasound for In Vivo Hemostasis Assessment Postcardiac Catheterization, Part I: Preclinical Studies

In this second of a two part series, we present pilot clinical data demonstrating Acoustic Radiation Force Impulse (ARFI) ultrasound for monitoring the onset of subcutaneous hemostasis at femoral artery puncture sites (arteriotomies), in vivo. We conducted a randomized, reader-blinded investigation of 20 patient volunteers who underwent diagnostic percutaneous coronary catheterization. After sheath removal (6 French), patients were randomized to treatment with either standard of care manual compression alone or, to expedite hemostasis, manual compression augmented with a p-GlcNAc fiber-based hemostatic dressing (Marine Polymer Technologies, Danvers MA). Concurrent with manual compression, serial ARFI imaging began at the time of sheath removal and continued every minute for 15 min. Serial data sets were processed with custom software to (1) estimate the time of hemostasis onset, and (2) render hybrid ARFI/B-Mode images to highlight displacements considered to correspond to extravasted blood. Images were read by an observer blinded to the treatment groups. Average estimated times to hemostasis in patient volunteers treated with manual compression alone (n = 10) and manual compression augmented by hemostatic dressing (n = 9) were, respectively, 13.00 ± 1.56 and 9.44 ± 3.09 min, which are statistically significantly different (p = 0.0065, Wilcoxon two-sample test). Example images are shown for three selected patient volunteers. These pilot data suggest that ARFI ultrasound is relevant to monitoring subcutaneous bleeding from femoral arteriotomies clinically and that time to hemostasis was significantly reduced by use of the hemostatic dressing

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 to 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 is inclusive of low to relatively high noise levels (up to 5dB average SNR to amplitude) but no outliers: for example, 99.53% correct for robust techniques versus 97.75% correct for the classical approach. The robust techniques also perform better than conventional approaches when ARFI data is inclusive of moderately high noise levels (10dB 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 versus 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

Acoustic radiation force beam sequence performance for detection and material characterization of atherosclerotic plaques: preclinical, ex vivo results

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 and specificity 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