Browsing by Author "Randles, Amanda"

Background

Methods and results

Conclusions

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for more than 17 million deaths per year, a number that is expected to double by 2030. Atherosclerosis is the most common manifestation of CVDs, affecting large and medium-sized blood vessels such as coronary arteries. There exists strong evidence that the development and progression of atherosclerosis is fundamentally driven by local hemodynamic forces, flow-generated endothelial shear stress (ESS), and pressure-derived tensile stress. These hemodynamic forces can be derived from sophisticated computational fluid dynamics (CFD) simulations. Personalized 3D CFD simulations based on patient-specific imaging data enable the characterization of physiological pressure and velocity fields. From these measures, critical diagnostic metrics such as fractional flow reserve (FFR) that are known to be associated with disease localization and progression can be derived on a per patient basis.

Existing CFD-based frameworks face three key challenges. First, current CFD methods rely either on a simplified 3D arterial geometry or physics-based algorithm. Such simplifications result in low-fidelity biomechanical models which limit accurate analysis of the full arterial tree and computation of local hemodynamic quantities. Second, traditional diagnostic metrics computed by these methods are only one facet of the disturbed intracoronary hemodynamics that are prevalent when complex coronary lesions and complex coronary anatomies are present or patients within the grey-zone patients with adverse outcomes. Thus, there is a need for an improved diagnostic metric that takes into account both morphological parameters and hemodynamic factors to define a unique phenotypic profile for such patients. Third, to ultimately enable clinical integration of CFD-based models, intuitive arterial visualization techniques are needed to allow clinicians to interact with the simulation data. Therefore, there is an \textit{urgent} need to address these challenges in order to prevent patients with coronary disease from having long-term, adverse cardiac complications.

The goal of the work was to improve our understanding of the relationship between hemodynamics and coronary artery diseases, and subsequently its diagnosis and treatment. To this end, we enabled accurate and non-invasive diagnostic assessment of coronary artery diseases. We furthermore identified key factors predictive of adverse events, minimized time to solution and devised intuitive interaction techniques. To achieve this goal, we first improved the physics-based CFD algorithm to reduce memory footprint and evaluated its scaling efficiency on traditional high performance computing (HPC) systems and cloud infrastructures. Second, we developed a cardiovascular modeling framework using routine patient imaging data for accurately computing critical hemodynamic variables and diagnostic metrics with the capability to harness widely available imaging data to enable large-scale retrospective and prospective studies. Finally, to ascertain translational potential of the work, we evaluated intuitive display modalities for physicians to interact with simulation data by conducting clinical user evaluation. Through these evaluation we assessed the application of state-of-art display methods, such as virtual and extended reality, for arterial visualization and treatment planning.

Current clinical gold standards offer only one facet of the disturbed intracoronary hemodynamics that are prevalent in certain CVD patient populations, for example in 1) complex coronary lesions and complex coronary anatomies 2) the grey-zone FFR [0.81-0.85] patients. Thus, this work details an accurate computational modeling framework which enables understanding the relationship between intracoronary hemodynamics variables, clinical metrics and arterial anatomy with the goal to guide therapeutic action. The key clinical and biomedical findings of this study show that ESS can differentiate complex and non-complex coronary lesions. Furthermore, we establish vorticity as a biomarker to distinguish grey-zone fractional flow reserve patients with future adverse cardiovascular events from those without such events in a multicenter clinical trial. To achieve computationally tractable runtimes on fixed HPC resources, we developed an implementation of our CFD solver that reduced memory by 74\% and determined appropriate and efficient boundary conditions. We also demonstrated the importance of anatomic detail in obtaining high fidelity biomechanical models using routine coronary angiographic imaging data. And validated against in vitro experiments and showed high diagnostic performance by comparing to in vivo data (n=200). For intuitive visualization of CFD data, we determined the influence of immersion using extended reality displays in treatment planning scenarios, e.g., percutaneous coronary intervention and coronary artery bypass graft. Therefore, we believe the work described in this dissertation would enable translational discovery using simulation-driven diagnostics and treatment planning, with the overall mission to improve clinical diagnosis and outcome for patients suffering from CVDs.

Background

Methods

Results

Conclusion

Metastatic cancer, the leading cause of cancer mortality, involves the complex process of circulating tumor cells (CTCs) spreading through the bloodstream and forming secondary tumors far away from the primary mass, which often travel a distance many thousand times its size. The interactions between CTCs and their neighboring red blood cells (RBCs), as well as the local hemodynamics in vessels, play a crucial role in determining these cells' fate; however, the mechanisms guiding their transit are still unclear. Predicting secondary tumor sites remains challenging due to the intricate dynamics of CTC migration. Thus, there remains a need to understand the interplay between the fluid dynamics, intercellular interactions, and vessel topology which can determine the fate of the CTC and subsequent likelihood of cancer progression.

Investigating CTC transport has involved a range of \textit{in vivo} and \textit{in vitro} studies to unravel the intricate mechanisms that dictate cellular outcomes. However, the process of tracking an individual CTC's trajectory through the massive vascular system is still not possible today \textit{in vivo}. The integration of \textit{in silico} models has proven instrumental, complementing traditional experimental approaches. In this work, we utilize our advanced fluid dynamics solver HARVEY to perform high-fidelity hemodynamic simulations to capture CTC dissemination. We outline several key contributions, including the addition of new physics interactions models and software optimizations, to enable these simulations to better capture biological phenomena and run to completion within a reasonable timeframe.

Numerical optimizations for \textit{in silico} models are still necessary: the drastic difference in length scales of CTC size versus distance traveled hinders current simulation models. To accurately capture intercellular dynamics, interactions must be modeled with sub-micrometer precision; meanwhile, the characteristic length scale of CTC traversal through the blood stream can be on the order of hundreds of millimeters, over 5 orders of magnitude larger. Numerically modeling hundreds of millions of individual cells at a sub-micron resolution over this timescale would require the entirety of multiple leadership-class supercomputers over the course of several weeks, if not months. Therefore, there still exists a disparity between these two ranges that needs to be addressed to make simulations of CTC transport with the presence of neighboring RBCs tractable.

We also address one of the pillars of the inherent variability in cell transport: the fate of a single CTC can exhibit significant variations due to its interactions with neighboring RBCs in the context of an \textit{in vivo} experiment,. A single simulation of a CTC may not encompass the range of outcomes, necessitating the consideration of many simulations with different RBC distributions. Multiplying the number of simulations required to capture this variability by the computational workload of a single simulation results in a computationally intractable workload, making it essential to optimize the number of simulations required for proper results. The number of potential cell configurations is vast, which makes it essential to identify representative configurations that encompass the full range of possible outcomes while optimizing computational feasibility.

This dissertation explores the influence of several hemodynamic and geometric parameters, microvasculature interactions, and the impact of RBCs on CTC movement, including the presence of RBC aggregation, RBC volume fraction, microvessel size, and shear rate. Furthermore, it discusses the enhancement of adaptive physics refinement methods to model cellular transport phenomena and highlights the capabilities of fluid-structure interaction models in capturing the dynamics of CTCs and RBCs across the system-scale. The dissertation concludes by discussing the development of a novel framework to account for the range of outcomes in CTC transport due to the variability in neighboring RBCs; it addresses the importance of generating representative configurations using quantitative metrics such as the Jaccard index applied to sets of sphere and RBC data sets. By integrating these advances, we further reduce the gap towards biologically accurate computational models of cancer cell transport, which holds promise for improving our understanding of cancer metastasis and developing effective strategies for cancer treatment.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are continuing to increase in prevalence throughout the world. While significant strides have been made towards reducing the morbidity and mortality of CVD, many current interventional and treatment strategies are associated with high rates of patient complications. A critical component behind the high complication rate is the complexity of the underlying patient hemodynamics and its relationships with vascular disease, but understanding this interplay is essential in order to better predict the progression of vascular disease and improve interventional planning. To investigate disease progression, we studied hemodynamics in two aortic diseases: Stanford type B aortic dissection (TBAD) and coarctation of the aorta (CoA). To examine and identify the role of personalized simulation in interventional planning, we studied venoarterial extracorporeal membrane oxygenation (VA-ECMO), a treatment system for patients with cardiopulmonary failure. These hemodynamic relationships are difficult to assess in vivo, but, in this work, we developed computational models to simulate blood flow, offering faster, cheaper, and more adaptable alternatives. While simulations provide a highly practical method of exploring these disease applications, the immense computational demand of simulations in large arteries limits their applicability in clinical settings and their tractability for widespread research purposes. Thus, this work also focused on the development and application of hemodynamic modeling algorithms with reduced computational cost, leading to more accessible frameworks. For patients supported by VA-ECMO, we developed multiscale modeling frameworks to probe the influence of VA-ECMO on pathological cerebral hemodynamics and its relationship with neurological complications. Our multiscale modeling framework implemented high-fidelity massively parallel three-dimensional (3D) models in the cerebral vasculature with low-fidelity one-dimensional models in the lower body to reduce the computational demand. We found that increasing the VA-ECMO flow rate compared with cardiac output reduced the total cerebral blood flow to the brain. Additionally, a VA-ECMO flow fraction of over 75% was needed to fully perfuse the cerebral arteries with oxygenated blood. For the aortic disease models, we combined simulation with machine learning to improve the modeling pipeline and reduce the computational demand. In TBAD patients, we used 3D models to discover relationships between local hemodynamics and aneurysmal degeneration and found that flow rate to the false lumen could supplement current indicators for patient intervention. In patients with CoA, we developed a design of experiments framework with machine learning to predict the pressure drop across and local flow downstream of a stenosis subject to a variety of co-factors, reducing the computational demand of simulations by ~5.6x. This framework can be used to probe the effects of co-factors on clinical hemodynamic metrics in a variety of geometries and vascular diseases. To further reduce the computational cost of aortic diseases models by nearly 9.8x, we combined deep learning with simulation to develop a framework capable of predicting pulsatile hemodynamics from a steady state solution. Altogether, this work can be broadly grouped into two categories: 1) investigating treatment options with multiscale models that combine low and high-fidelity simulations to target specific regions of interest and 2) predicting CVD progression and patient risk by combining simulation with machine learning to reduce the number of simulations needed and the computational cost of each simulation. The disease applications that we studied and the frameworks that we developed can be used by researchers and clinicians to model hemodynamics and improve treatment strategies and clinical decisions for patients with CVD.

Metastasis causes most cancer-related mortality; thus, it is of paramount importance that science fully understands the factors influencing the fate of cancerous cells. Therefore, a key area of study is how the transport of tumor cells from the primary tumor via the bloodstream is affected by both the cells’ interactions with the endothelium and local hemodynamics in microvessels. In circulating tumor cells (CTCs) traveling through the cardiovascular system, the mechanism by which the trajectory of CTCs is guided and cells attach to the endothelium is not fully understood. Current studies with in silico, in vitro, and in vivo models have not been able to untangle the unclear interplay between hemodynamics and cell-cell interactions in determining a cancer cell’s trajectory and location of secondary metastases—stressing the importance of capturing multiple sources of interaction.

Using state-of-the-art computational models to simulate the local microenvironment of a circulating tumor cell (CTC) provides a promising method of recapitulating the complex in vivo setting while maintaining control over parameters to better understand tumor cell transport. The interplay between forces imparted by fluid dynamics and cell-cell interactions on deformable CTCs contributes to the complexity of disease-specific models. The length scales across which cells travel pose an additional set of problems, with the need to bridge dissimilar length scales ranging from the sub-micron scale for cell interactions, micron scale for individual cell deformation, and larger to resolve the fluid dynamics of cells in vessels ranging from capillaries to venules and larger. Thus, there is an unmet need for studies and computational techniques which can integrate the multiple factors impacting CTC transport in microvessels.

Towards unraveling the factors influencing cancer cell transport and preferential metastasis in certain tissues; this dissertation required the development of a next-generation framework for the computational study of cancer cell transport. In service of that goal, we have advanced both the size and efficiency of deformable cancer cell simulations. These novel methods enable high hematocrit distributed simulations which we demonstrated to be up to eighteen times faster than comparable simulations while simulating millions of deformable cells. Simulations of millions of deformable erythrocytes were completed using the state- of-the-art GPU-accelerated fluid structure interaction model we have developed and are, to our knowledge, the largest of such simulations. Given the importance of erythrocytes in impacting CTC trajectory, it is important to capture these interactions in an efficient manner. The framework presented in this dissertation additionally captures the microscale interactions between tumor cells and their microenvironment which we have used to study the impact of endothelial receptor patterns on cell transport. We found that adhesive cells were affected by the pattern of endothelial receptors with larger patches causing changes in cell lateral position in curved microvessels, slowing cells in straight microvessels, and impacting branch path in bifurcations.

These advances were coupled together into a novel model, which we term adaptive physics refinement (APR), that captures the target CTC at the sub-micron resolution required for biologically-relevant simulation and increases the simulation domain to cellular-scale resolution at previously intractable volumes. With the APR model, we showed that simulation domain sizes > 100 mL were attainable, whereas traditional methods were limited to approximately 10^{-2} mL simulated volume given similar computing resources. Furthermore, APR now permits more flexibility in research study design by lowering the cost of each simulation to enable larger computational experiments. Collectively, we expect these advances to set the stage for disease-specific models of cancer metastasis which will aid in the development of new diagnostics and therapeutics.