Carotid Plaque Fibrous Cap Thickness Measurement by ARFI Variance of Acceleration: In Vivo Human Results

The microenvironment of the atheroma expresses phenotypes of plaque instability

Data from histopathology studies of human atherosclerotic tissue specimens and from vascular imaging studies support the concept that the local arterial microenvironment of a stable atheroma promotes destabilizing conditions that result in the transition to an unstable atheroma. Destabilization is characterized by several different plaque phenotypes that cause major clinical events such as acute coronary syndrome and cerebrovascular strokes. There are several rupture-associated phenotypes causing thrombotic vascular occlusion including simple fibrous cap rupture of an atheroma, fibrous cap rupture at site of previous rupture-and-repair of an atheroma, and nodular calcification with rupture. Endothelial erosion without rupture has more recently been shown to be a common phenotype to promote thrombosis as well. Microenvironment features that are linked to these phenotypes of plaque instability are neovascularization arising from the vasa vasorum network leading to necrotic core expansion, intraplaque hemorrhage, and cap rupture; activation of adventitial and perivascular adipose tissue cells leading to secretion of cytokines, growth factors, adipokines in the outer artery wall that destabilize plaque structure; and vascular smooth muscle cell phenotypic switching through transdifferentiation and stem/progenitor cell activation resulting in the promotion of inflammation, calcification, and secretion of extracellular matrix, altering fibrous cap structure, and necrotic core growth. As the technology evolves, studies using noninvasive vascular imaging will be able to investigate the transition of stable to unstable atheromas in real time. A limitation in the field, however, is that reliable and predictable experimental models of spontaneous plaque rupture and/or erosion are not currently available to study the cell and molecular mechanisms that regulate the conversion of the stable atheroma to an unstable plaque.

Comparing Focused-Tracked and Plane Wave-Tracked ARFI Log(VoA) In Silico and in Application to Human Carotid Atherosclerotic Plaque, Ex Vivo

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.

BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.