Carotid artery wall stiffness has been widely considered as an index of vascular health, and has been associated with occurrence of cardiovascular events, such as stroke. In addition, the blood flow patterns in the carotid artery can yield crucial information on atherosclerosis progression and cerebrovascular impairment. Pulse wave imaging (PWI) is a non-invasive ultrasound imaging technique that tracks the propagation of the arterial pulse wave, providing thus regional arterial wall stiffness mapping. Moreover, towards enabling accurate visualization of blood flow patterns, ultrasound-based vector flow imaging (VFI) modalities have been developed.
Building upon PWI and VFI techniques, the overall goal of this dissertation is to develop ultrasound-based methodologies that can provide simultaneous imaging of the carotid artery wall mechanics and blood flow dynamics at high temporal and spatial resolutions. The developed techniques are validated through vessel phantom experiments and simulations. Furthermore, their potential to diagnose pre-clinical stages of carotid artery disease and provide additional insights in risk for stroke assessment, is demonstrated in an atherosclerotic swine study and human subjects in vivo. More specifically:
A method is presented that analyzes the pattern of arterial wall motion derived by PWI, in order to detect spatial mechanical inhomogeneity across an imaged artery, and provide piecewise arterial wall stiffness estimates. The proposed technique is validated in a phantom consisting of a soft and a stiff segment, while its feasibility is demonstrated to identify inhomogeneous wall properties in atherosclerotic human carotid arteries, as well as provide atherosclerotic plaque mechanical characterization in vivo.
Subsequently, PWI is integrated with VFI techniques in the same ultrasound acquisition sequence, in order to enable simultaneous and co-localized imaging of arterial wall stiffness and blood vector flow velocity. The performance of the technique is investigated through experiments and FSI simulations. Moreover, its feasibility was shown to investigate associations between carotid artery Pulse Wave Velocity and blood flow patterns, in vivo.
Based on the previously developed PWI and VFI modalities, a novel ultrasound-based technique is developed that combines high frame rate vector flow imaging with a data clustering approach, in order to enable direct and robust wall shear stress measurements. The performance of the proposed method is evaluated through vessel phantom experiments and simulations, while its feasibility is shown to detect pre-clinical stages of carotid artery disease in a swine model in vivo. In addition, a pilot clinical study is presented involving application of the developed modality in normal and atherosclerotic human carotid arteries in-vivo.
Moving forward, the developed imaging modalities are used to implement novel clinical biomarkers based on carotid artery arterial wall mechanics and blood flow dynamics, that can potentially assist in risk for stroke assessment. The patterns of those biomarkers are investigated in the common carotid arteries of subjects with low degree of stenosis and medical history of stroke, against subjects without history of stroke. The same biomarkers are also analyzed with respect to stroke symptomatology in atherosclerotic patients with moderate to high degree of stenosis. Moreover, the developed techniques are used to identify vulnerable plaque components in subjects with fully developed plaques, as compared with CTA scans.
Finally, a deep learning-based approach for motion tracking of the arterial wall throughout the cardiac cycle is proposed. A neural network is trained to learn the motion patterns of the carotid artery and potentially improve the quality of PWI. The performance of the technique is assessed in vessel phantom experiments and its feasibility is demonstrated in healthy human carotid arteries in-vivo.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/14a8-xv97 |
| Date | January 2022 |
| Creators | Karageorgos, Grigorios Marios |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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