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Open Access Publications from the University of California

Scholarly Works (20 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2015)

Patients with coronary artery aneurysms (CAA) resulting from Kawasaki Disease (KD) are at risk for thrombosis and myocardial infarction. Current guidelines recommend using CAA diameter >8 mm as the criterion for initiating systemic anticoagulation, but there is little outcome data to support this choice. Transluminal Attenuation Gradient (TAG) has been proposed as a non-invasive method for evaluating the functional significance of coronary stenoses using CT Angiography (CTA). However TAG has not previously been used to assess CAA. We hypothesized that abnormal flow dynamics in CAA caused by KD could be quantified using TAG. We quantified TAG in the major coronary arteries by performing linear regression of the average lumen intensity as a function of vessel length and compared TAG values for aneurysmal and normal arteries. Aneurysm geometry was characterized using maximum aneurysm diameter, aneurysm shape index and sphericity index. TAG of aneurysmal arteries was significantly lower than in normal arteries. Significant differences were also seen between aneurysmal vs. normal left anterior descending and right coronary. Geometrical parameters showed minimal to no correlation with TAG. This study is the first application of TAG analysis to CAA caused by KD, and demonstrates significantly different TAG values in aneurysmal versus normal arteries. Lack of correlation between TAG and CAA geometry suggests that TAG may provide information on hemodynamic conditions not available from anatomy alone. The use of TAG in KD patients represents a possible extension to standard CTA that could aid in clinical decision-making and help to better evaluate the risk of thrombus formation.

    Cover page: Assessment of Coronary Artery Aneurysms Cased By Kawasaki Diesease Using Transluminal Attenuation Gradient Analysis of CT Angiograms
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2023)
    Mechanical forces are essential for coordinating cardiac morphogenesis, but much remains to be discovered about the interactions between mechanical forces and the mechanotransduction pathways they activate. Due to the elaborate and fundamentally multi-physics and multi-scale nature of cardiac mechanobiology, a complete understanding requires multiple experimental and analytical techniques. We identify three fundamental tools used in the field to probe these interactions: high resolution imaging, genetic and molecular analysis, and computational modeling. In this review, we focus on computational modeling and present recent studies employing this tool to investigate the mechanobiological pathways involved with cardiac development. These works demonstrate that understanding the detailed spatial and temporal patterns of biomechanical forces is crucial to building a comprehensive understanding of mechanobiology during cardiac development, and that computational modeling is an effective and efficient tool for obtaining such detail. In this context, multidisciplinary studies combining all three tools present the most compelling results.
      Cover page: Recent advances in quantifying the mechanobiology of cardiac development via computational modeling.
      • Thesis
      • Peer Reviewed
      UC San Diego Electronic Theses and Dissertations (2017)

      Vein graft maladaptation, leading to poor long-term patency, is a serious clinical problem in patients receiving coronary artery bypass grafts (CABGs). Mechanics is known to play a key role as a stimulus contributing towards vein graft failure. Mechanical stimuli are key to understanding disease progression and clinically observed differences in failure rates between arterial and venous grafts following coronary artery bypass graft surgery. But little has been done to quantify the mechanics in these grafts and its effects on long-term outcomes on grafts. Hence, one of the goals of this thesis was to quantify mechanical stimuli acting on the grafts and the other goal was to develop continuum mechanics based models of growth and remodeling (G&R) to simulate long-term adaptation.

      We quantify biologically relevant mechanical stimuli, not available from standard imaging, in patient-specific simulations incorporating non-invasive clinical data. We couple computational fluid dynamics with closed-loop circulatory physiology models to quantify biologically relevant indices, including wall shear, oscillatory shear, and wall strain. We account for vessel-specific material properties in simulating vessel wall deformation. Wall shear was significantly lower and atheroprone area significantly

      higher in venous compared to arterial grafts. Wall strain in venous grafts was significantly lower than in arterial grafts while no significant difference was observed in oscillatory shear index. Simulations demonstrate significant differences in mechanical stimuli acting on venous vs. arterial grafts, in line with clinically observed graft failure rates, offering a promising avenue for stratifying patients at risk for graft failure.

      We also propose a computational model of venous adaptation to altered hemodynamics based on a constrained mixture theory of G&R. We identify constitutive parameters that optimally match biaxial data from a mouse vena cava, then numerically subject the vein to altered hemodynamic conditions and quantify the extent of adaptation. We identify constitutive relations and parameters that enable adap-

      tations for a moderate perturbation in hemodynamics. We then fix these relations and parameters, and subject the vein to a range of combined loads (pressure and flow), from moderate to severe, and identify plausible mechanisms of adaptation versus maladaptation. We also explore the beneficial effects of a gradual increase in load on adaptation.

        Cover page: Towards a Fluid-Structure-Growth and Remodeling Framework to Simulate Vein Graft Failure Post Coronary Artery Bypass Surgery
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2022)
        In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies.
          Cover page: An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricleCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2014)
          To perform experimental validation of computational fluid dynamics (CFD) applied to patient specific coronary aneurysm anatomy of Kawasaki disease. We quantified hemodynamics in a patient-specific coronary artery aneurysm physical phantom under physiologic rest and exercise flow conditions. Using phase contrast MRI (PCMRI), we acquired 3-component flow velocity at two slice locations in the aneurysms. We then performed numerical simulations with the same geometry and inflow conditions, and performed qualitative and quantitative comparisons of velocities between experimental measurements and simulation results. We observed excellent qualitative agreement in flow pattern features. The quantitative spatially and temporally varying differences in velocity between PCMRI and CFD were proportional to the flow velocity. As a result, the percent discrepancy between simulation and experiment was relatively constant regardless of flow velocity variations. Through 1D and 2D quantitative comparisons, we found a 5-17% difference between measured and simulated velocities. Additional analysis assessed wall shear stress differences between deformable and rigid wall simulations. This study demonstrated that CFD produced good qualitative and quantitative predictions of velocities in a realistic coronary aneurysm anatomy under physiological flow conditions. The results provide insights on factors that may influence the level of agreement, and a set of in vitro experimental data that can be used by others to compare against CFD simulation results. The findings of this study increase confidence in the use of CFD for investigating hemodynamics in the specialized anatomy of coronary aneurysms. This provides a basis for future hemodynamics studies in patient-specific models of Kawasaki disease.
            Cover page: In Vitro Validation of Patient-Specific Hemodynamic Simulations in Coronary Aneurysms Caused by Kawasaki Disease
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2022)
            Cardiothoracic surgeons are faced with a choice of different revascularization techniques and diameters for saphenous vein grafts (SVG) in coronary artery bypass graft surgery . Using computational simulations, we virtually investigate the effect of SVG geometry on hemodynamics of both venous grafts and the target coronary arteries. We generated patient-specific 3-dimensional anatomic models of coronary artery bypass graft surgery patients and quantified mechanical stimuli. We performed virtual surgery on 3 patient-specific models by modifying the geometry vein grafts to reflect single, Y, and sequential surgical configurations with SVG diameters ranging from 2 mm to 5 mm. Our study demonstrates that the coronary artery runoffs are relatively insensitive to the choice of SVG revascularization geometry. We observe a 10% increase in runoff when the SVG diameter is changed from 2 mm to 5 mm. The wall shear stress of SVG increases dramatically when the diameter drops, following an inverse power scaling with diameter. For a fixed diameter, the average wall shear stress on the vein graft varies in ascending order as single, Y, and sequential graft in the patient cohort. The runoff to the target coronary arteries changes marginally due to the choice of graft configuration or diameter. The shear stress on the vein graft depends on both flow rate and diameter and follows an inverse power scaling consistent with a Poiseuille flow assumption. Given the similarity in runoff with different surgical configurations, choices of SVG geometries can be informed by propensity for graft failure using shear stress evaluations.
              Cover page: Computational Evaluation of Venous Graft Geometries in Coronary Artery Bypass Surgery
              • Article
              • Peer Reviewed
              LBL Publications (2022)
              Collateral circulation in the circle of Willis (CoW), closely associated with disease mechanisms and treatment outcomes, can be effectively investigated using one-dimensional-zero-dimensional hemodynamic simulations. As the entire cardiovascular system is considered in the simulation, it captures the systemic effects of local arterial changes, thus reproducing collateral circulation that reflects biological phenomena. The simulation facilitates rapid assessment of clinically relevant hemodynamic quantities under patient-specific conditions by incorporating clinical data. During patient-specific simulations, the impact of clinical data uncertainty on the simulated quantities should be quantified to obtain reliable results. However, as uncertainty quantification (UQ) is time-consuming and computationally expensive, its implementation in time-sensitive clinical applications is considered impractical. Therefore, we constructed a surrogate model based on machine learning using simulation data. The model accurately predicts the flow rate and pressure in the CoW in a few milliseconds. This reduced computation time enables the UQ execution with 100 000 predictions in a few minutes on a single CPU core and in less than a minute on a GPU. We performed UQ to predict the risk of cerebral hyperperfusion (CH), a life-threatening condition that can occur after carotid artery stenosis surgery if collateral circulation fails to function appropriately. We predicted the statistics of the postoperative flow rate increase in the CoW, which is a measure of CH, considering the uncertainties of arterial diameters, stenosis parameters, and flow rates measured using the patients' clinical data. A sensitivity analysis was performed to clarify the impact of each uncertain parameter on the flow rate increase. Results indicated that CH occurred when two conditions were satisfied simultaneously: severe stenosis and when arteries of small diameter serve as the collateral pathway to the cerebral artery on the stenosis side. These findings elucidate the biological aspects of cerebral circulation in terms of the relationship between collateral flow and CH.
                Cover page: Uncertainty quantification in cerebral circulation simulations focusing on the collateral flow: Surrogate model approach with machine learning.
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2017)
                Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic, translational and clinical research. In this paper we discuss the recent development of a fully open-source SimVascular software package, which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis. This package serves as a research tool for cardiovascular modeling and simulation, and has contributed to numerous advances in personalized medicine, surgical planning and medical device design. The SimVascular software has recently been refactored and expanded to enhance functionality, usability, efficiency and accuracy of image-based patient-specific modeling tools. Moreover, SimVascular previously required several licensed components that hindered new user adoption and code management and our recent developments have replaced these commercial components to create a fully open source pipeline. These developments foster advances in cardiovascular modeling research, increased collaboration, standardization of methods, and a growing developer community.
                  Cover page: SimVascular: An Open Source Pipeline for Cardiovascular Simulation
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2014)
                  Image-based in silico modeling tools provide detailed velocity and particle deposition data. However, care must be taken when prescribing boundary conditions to model lung physiology in health or disease, such as in emphysema. In this study, the respiratory resistance and compliance were obtained by solving an inverse problem; a 0D global model based on healthy and emphysematous rat experimental data. Multi-scale CFD simulations were performed by solving the 3D Navier-Stokes equations in an MRI-derived rat geometry coupled to a 0D model. Particles with 0.95 μm diameter were tracked and their distribution in the lung was assessed. Seven 3D-0D simulations were performed: healthy, homogeneous, and five heterogeneous emphysema cases. Compliance (C) was significantly higher (p = 0.04) in the emphysematous rats (C = 0.37 ± 0.14 cm(3)/cmH2O) compared to the healthy rats (C = 0.25 ± 0.04 cm(3)/cmH2O), while the resistance remained unchanged (p = 0.83). There were increases in airflow, particle deposition in the 3D model, and particle delivery to the diseased regions for the heterogeneous cases compared to the homogeneous cases. The results highlight the importance of multi-scale numerical simulations to study airflow and particle distribution in healthy and diseased lungs. The effect of particle size and gravity were studied. Once available, these in silico predictions may be compared to experimental deposition data.
                    Cover page: Airflow and Particle Deposition Simulations in Health and Emphysema: From In Vivo to In Silico Animal Experiments
                    • Article
                    • Peer Reviewed
                    UC San Diego Previously Published Works (2014)
                    Kawasaki disease (KD) is the leading cause of acquired heart disease in children and can result in life-threatening coronary artery aneurysms in up to 25 % of patients. These aneurysms put patients at risk of thrombus formation, myocardial infarction, and sudden death. Clinicians must therefore decide which patients should be treated with anticoagulant medication, and/or surgical or percutaneous intervention. Current recommendations regarding initiation of anticoagulant therapy are based on anatomy alone with historical data suggesting that patients with aneurysms [Formula: see text]8 mm are at greatest risk of thrombosis. Given the multitude of variables that influence thrombus formation, we postulated that hemodynamic data derived from patient-specific simulations would more accurately predict risk of thrombosis than maximum diameter alone. Patient-specific blood flow simulations were performed on five KD patients with aneurysms and one KD patient with normal coronary arteries. Key hemodynamic and geometric parameters, including wall shear stress, particle residence time, and shape indices, were extracted from the models and simulations and compared with clinical outcomes. Preliminary fluid structure interaction simulations with radial expansion were performed, revealing modest differences in wall shear stress compared to the rigid wall case. Simulations provide compelling evidence that hemodynamic parameters may be a more accurate predictor of thrombotic risk than aneurysm diameter alone and motivate the need for follow-up studies with a larger cohort. These results suggest that a clinical index incorporating hemodynamic information be used in the future to select patients for anticoagulant therapy.
                      Cover page: Thrombotic risk stratification using computational modeling in patients with coronary artery aneurysms following Kawasaki disease
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