Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve.

BACKGROUND: Computational fluid dynamics can compute fractional flow reserve (FFR) accurately. However, existing models are limited by either the intravascular hemodynamic phenomarkers that can be captured or the fidelity of geometries that can be modeled. METHODS AND RESULTS: This study aimed to validate a new coronary angiography-based FFR framework, FFRHARVEY, and examine intravascular hemodynamics to identify new biomarkers that could augment FFR in discerning unrevascularized patients requiring intervention. A 2-center cohort was used to examine diagnostic performance of FFRHARVEY compared with reference wire-based FFR (FFRINVASIVE). Additional biomarkers, longitudinal vorticity, velocity, and wall shear stress, were evaluated for their ability to augment FFR and indicate major adverse cardiac events. A total of 160 patients with 166 lesions were investigated. FFRHARVEY was compared with FFRINVASIVE by investigators blinded to the invasive FFR results with a per-stenosis area under the curve of 0.91, positive predictive value of 90.2%, negative predictive value of 89.6%, sensitivity of 79.3%, and specificity of 95.4%. The percentage ofdiscrepancy for continuous values of FFR was 6.63%. We identified a hemodynamic phenomarker, longitudinal vorticity, as a metric indicative of major adverse cardiac events in unrevascularized gray-zone cases. CONCLUSIONS: FFRHARVEY had high performance (area under the curve: 0.91, positive predictive value: 90.2%, negative predictive value: 89.6%) compared with FFRINVASIVE. The proposed framework provides a robust and accurate way to compute a complete set of intravascular phenomarkers, in which longitudinal vorticity was specifically shown to differentiate vessels predisposed to major adverse cardiac events.

Surgical Modulation of Pulmonary Artery Shear Stress: A Patient-Specific CFD Analysis of the Norwood Procedure.

PURPOSR: This study created 3D CFD models of the Norwood procedure for hypoplastic left heart syndrome (HLHS) using standard angiography and echocardiogram data to investigate the impact of shunt characteristics on pulmonary artery (PA) hemodynamics. Leveraging routine clinical data offers advantages such as availability and cost-effectiveness without subjecting patients to additional invasive procedures. METHODS: Patient-specific geometries of the intrathoracic arteries of two Norwood patients were generated from biplane cineangiograms. "Virtual surgery" was then performed to simulate the hemodynamics of alternative PA shunt configurations, including shunt type (modified Blalock-Thomas-Taussig shunt (mBTTS) vs. right ventricle-to-pulmonary artery shunt (RVPAS)), shunt diameter, and pulmonary artery anastomosis angle. Left-right pulmonary flow differential, Qp/Qs, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated. RESULTS: There was strong agreement between clinically measured data and CFD model output throughout the patient-specific models. Geometries with a RVPAS tended toward more balanced left-right pulmonary flow, lower Qp/Qs, and greater TAWSS and OSI than models with a mBTTS. For both shunt types, larger shunts resulted in a higher Qp/Qs and higher TAWSS, with minimal effect on OSI. Low TAWSS areas correlated with regions of low flow and changing the PA-shunt anastomosis angle to face toward low TAWSS regions increased TAWSS. CONCLUSION: Excellent correlation between clinically measured and CFD model data shows that 3D CFD models of HLHS Norwood can be developed using standard angiography and echocardiographic data. The CFD analysis also revealed consistent changes in PA TAWSS, flow differential, and OSI as a function of shunt characteristics.

Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles.

Continuously measured arterial blood velocity can provide insight into physiological parameters and potential disease states. The efficient and effective description of the temporal profiles of arterial velocity is crucial for both clinical practice and research. We propose a pipeline to identify the minimum number of points of interest to adequately describe a velocity profile of the left coronary artery. This pipeline employs a novel operation that "stretches" a baseline waveform to quantify the utility of a point in fitting other waveforms. Our study introduces a comprehensive pipeline specifically designed to identify the minimal yet crucial number of points needed to accurately represent the velocity profile of the left coronary artery. Additionally, the only location-dependent portion of this pipeline is the first step, choosing all of the possible points of interest. Hence, this work is broadly applicable to other waveforms. This versatility paves the way for a novel non-frequency domain method that can enhance the analysis of physiological waveforms. Such advancements have potential implications in both research and clinical treatment of various diseases, underscoring the broader applicability and impact.

Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts.

Simulations of cancer cell transport require accurately modeling mm-scale and longer trajectories through a circulatory system containing trillions of deformable red blood cells, whose intercellular interactions require submicron fidelity. Using a hybrid CPU-GPU approach, we extend the advanced physics refinement (APR) method to couple a finely-resolved region of explicitly-modeled red blood cells to a coarsely-resolved bulk fluid domain. We further develop algorithms that: capture the dynamics at the interface of differing viscosities, maintain hematocrit within the cell-filled volume, and move the finely-resolved region and encapsulated cells while tracking an individual cancer cell. Comparison to a fully-resolved fluid-structure interaction model is presented for verification. Finally, we use the advanced APR method to simulate cancer cell transport over a mm-scale distance while maintaining a local region of RBCs, using a fraction of the computational power required to run a fully-resolved model.

Investigating the Influence of Heterogeneity Within Cell Types on Microvessel Network Transport.

Background: Current research on the biophysics of circulating tumor cells often overlooks the heterogeneity of cell populations, focusing instead on average cellular properties. This study aims to address the gap by considering the diversity of cell biophysical characteristics and their implications on cancer spread. Methods: We utilized computer simulations to assess the influence of variations in cell size and membrane elasticity on the behavior of cells within fluid environments. The study controlled cell and fluid properties to systematically investigate the transport of tumor cells through a simulated network of branching channels. Results: The simulations revealed that even minor differences in cellular properties, such as slight changes in cell radius or shear elastic modulus, lead to significant changes in the fluid conditions that cells experience, including velocity and wall shear stress (p < 0.001). Conclusion: The findings underscore the importance of considering cell heterogeneity in biophysical studies and suggest that small variations in cellular characteristics can profoundly impact the dynamics of tumor cell circulation. This has potential implications for understanding the mechanisms of cancer metastasis and the development of therapeutic strategies.

Modeling the effect of patient size on cerebral perfusion during veno-arterial extracorporeal membrane oxygenation.

INTRODUCTION: A well-known complication of veno-arterial extracorporeal membrane oxygenation (VA ECMO) is differential hypoxia, in which poorly-oxygenated blood ejected from the left ventricle mixes with and displaces well-oxygenated blood from the circuit, thereby causing cerebral hypoxia and ischemia. We sought to characterize the impact of patient size and anatomy on cerebral perfusion under a range of different VA ECMO flow conditions. METHODS: We use one-dimensional (1D) flow simulations to investigate mixing zone location and cerebral perfusion across 10 different levels of VA ECMO support in eight semi-idealized patient geometries, for a total of 80 scenarios. Measured outcomes included mixing zone location and cerebral blood flow (CBF). RESULTS: Depending on patient anatomy, we found that a VA ECMO support ranging between 67-97% of a patient's ideal cardiac output was needed to perfuse the brain. In some cases, VA ECMO flows exceeding 90% of the patient's ideal cardiac output are needed for adequate cerebral perfusion. CONCLUSIONS: Individual patient anatomy markedly affects mixing zone location and cerebral perfusion in VA ECMO. Future fluid simulations of VA ECMO physiology should incorporate varied patient sizes and geometries in order to best provide insights toward reducing neurologic injury and improved outcomes in this patient population.

The Role of Immersion for Improving Extended Reality Analysis of Personalized Flow Simulations.

PURPOSE: Computational models of flow in patient-derived arterial geometries have become a key paradigm of biomedical research. These fluid models are often challenging to visualize due to high spatial heterogeneity and visual complexity. Virtual immersive environments can offer advantageous visualization of spatially heterogeneous and complex systems. However, as different VR devices offer varying levels of immersion, there remains a crucial lack of understanding regarding what level of immersion is best suited for interactions with patient-specific flow models. METHODS: We conducted a quantitative user evaluation with multiple VR devices testing an important use of hemodynamic simulations-analysis of surface parameters within complex patient-specific geometries. This task was compared for the semi-immersive zSpace 3D monitor and the fully immersive HTC Vive system. RESULTS: The semi-immersive device was more accurate than the fully immersive device. The two devices showed similar results for task duration and performance (accuracy/duration). The accuracy of the semi-immersive device was also higher for arterial geometries of greater complexity and branching. CONCLUSION: This assessment demonstrates that the level of immersion plays a significant role in the accuracy of assessing arterial flow models. We found that the semi-immersive VR device was a generally optimal choice for arterial visualization.

Effect of constitutive law on the erythrocyte membrane response to large strains.

Three constitutive laws, that is the Skalak, neo-Hookean and Yeoh laws, commonly employed for describing the erythrocyte membrane mechanics are theoretically analyzed and numerically investigated to assess their accuracy for capturing erythrocyte deformation characteristics and morphology. Particular emphasis is given to the nonlinear deformation regime, where it is known that the discrepancies between constitutive laws are most prominent. Hence, the experiments of optical tweezers and micropipette aspiration are considered here, for which relationships between the individual shear elastic moduli of the constitutive laws can also be established through analysis of the tension-deformation relationship. All constitutive laws were found to adequately predict the axial and transverse deformations of a red blood cell subjected to stretching with optical tweezers for a constant shear elastic modulus value. As opposed to Skalak law, the neo-Hookean and Yeoh laws replicated the erythrocyte membrane folding, that has been experimentally observed, with the trade-off of sustaining significant area variations. For the micropipette aspiration, the suction pressure-aspiration length relationship could be excellently predicted for a fixed shear elastic modulus value only when Yeoh law was considered. Importantly, the neo-Hookean and Yeoh laws reproduced the membrane wrinkling at suction pressures close to those experimentally measured. None of the constitutive laws suffered from membrane area compressibility in the micropipette aspiration case.

Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps.

Tracking hemodynamic responses to treatment and stimuli over long periods remains a grand challenge. Moving from established single-heartbeat technology to longitudinal profiles would require continuous data describing how the patient's state evolves, new methods to extend the temporal domain over which flow is sampled, and high-throughput computing resources. While personalized digital twins can accurately measure 3D hemodynamics over several heartbeats, state-of-the-art methods would require hundreds of years of wallclock time on leadership scale systems to simulate one day of activity. To address these challenges, we propose a cloud-based, parallel-in-time framework leveraging continuous data from wearable devices to capture the first 3D patient-specific, longitudinal hemodynamic maps. We demonstrate the validity of our method by establishing ground truth data for 750 beats and comparing the results. Our cloud-based framework is based on an initial fixed set of simulations to enable the wearable-informed creation of personalized longitudinal hemodynamic maps.

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve.

Background: Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR baseline ), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines. Aims: To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR invasive ) as gold standard. Materials and Methods: Personalized coronary geometries ( N = 50 ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR baseline , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR baseline was validated against FFR invasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR streamlined was created by only incorporating the most sensitive inputs and FFR semi-streamlined additionally included patient-specific distal location. Results: FFR baseline was validated against FFR invasive via correlation ( r = 0.714 , p < 0.001 ), agreement (mean difference: 0.01 ± 0.09 ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR semi-streamlined provided identical diagnostic performance with FFR baseline . Compared to FFR baseline vs. FFR invasive , FFR streamlined vs. FFR invasive had decreased correlation ( r = 0.64 , p < 0.001 ), improved agreement (mean difference: 0.01 ± 0.08 ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). Conclusion: Streamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

The role of adhesive receptor patterns on cell transport in complex microvessels.

Cell transport is governed by the interaction of fluid dynamic forces and biochemical factors such as adhesion receptor expression and concentration. Although the effect of endothelial receptor density is well understood, it is not clear how the spacing and local spatial distribution of receptors affect cell adhesion in three-dimensional microvessels. To elucidate the effect of vessel shape on cell trajectory and the arrangement of endothelial receptors on cell adhesion, we employed a three-dimensional deformable cell model that incorporates microscale interactions between the cell and the endothelium. Computational cellular adhesion models are systematically altered to assess the influence of receptor spacing. We demonstrate that the patterns of receptors on the vessel walls are a key factor guiding cell movement. In straight microvessels, we show a relationship between cell velocity and the spatial distribution of adhesive endothelial receptors, with larger receptor patches producing lower translational velocities. The joint effect of the complex vessel topology seen in microvessel shapes such as curved and bifurcated vessels when compared to straight tubes is explored with results which showed the spatial distribution of receptors affecting cell trajectory. Our findings here represent demonstration of the previously undescribed relationship between receptor pattern and geometry that guides cellular movement in complex microenvironments.

Propagation pattern for moment representation of the lattice Boltzmann method.

A propagation pattern for the moment representation of the regularized lattice Boltzmann method (LBM) in three dimensions is presented. Using effectively lossless compression, the simulation state is stored as a set of moments of the lattice Boltzmann distribution function, instead of the distribution function itself. An efficient cache-aware propagation pattern for this moment representation has the effect of substantially reducing both the storage and memory bandwidth required for LBM simulations. This paper extends recent work with the moment representation by expanding the performance analysis on central processing unit (CPU) architectures, considering how boundary conditions are implemented, and demonstrating the effectiveness of the moment representation on a graphics processing unit (GPU) architecture.

Characterization of hemodynamics in anomalous aortic origin of coronary arteries using patient-specific modeling.

The anomalous aortic origin of coronary arteries (AAOCA) is a congenital disease that can lead to sudden cardiac death (SCD) during strenuous physical activity. Despite AAOCA being the second leading cause of SCD among young athletes, the mechanism behind sudden cardiac death remains mostly unknown. Computational fluid dynamics provides a powerful tool for studying how pathologic anatomy can affect different hemodynamic states. The present study investigates the effect of AAOCA on patient hemodynamics. We performed patient-specific hemodynamic simulations of interarterial AAOCA at baseline and in the exercise state using our massively parallel flow solver. Additionally, we investigate how surgical correction via coronary unroofing impacts patient blood flow. Results show that patient-specific AAOCA models exhibited higher interarterial time-averaged wall shear stress (TAWSS) values compared to the control patients. The oscillatory shear index had no impact on AAOCA. Finally, the coronary unroofing procedure normalized the elevated TAWSS by decreasing TAWSS in the postoperative patient. The present study provides a proof of concept for the potential hemodynamic factors underlying coronary ischemia in AAOCA during exercise state.

High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.

The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.

Distributed Acceleration of Adhesive Dynamics Simulations.

Cell adhesion plays a critical role in processes ranging from leukocyte migration to cancer cell transport during metastasis. Adhesive cell interactions can occur over large distances in microvessel networks with cells traveling over distances much greater than the length scale of their own diameter. Therefore, biologically relevant investigations necessitate efficient modeling of large field-of-view domains, but current models are limited by simulating such geometries at the sub-micron scale required to model adhesive interactions which greatly increases the computational requirements for even small domain sizes. In this study we introduce a hybrid scheme reliant on both on-node and distributed parallelism to accelerate a fully deformable adhesive dynamics cell model. This scheme leads to performant system usage of modern supercomputers which use a many-core per-node architecture. On-node acceleration is augmented by a combination of spatial data structures and algorithmic changes to lessen the need for atomic operations. This deformable adhesive cell model accelerated with hybrid parallelization allows us to bridge the gap between high-resolution cell models which can capture the sub-micron adhesive interactions between the cell and its microenvironment, and large-scale fluid-structure interaction (FSI) models which can track cells over considerable distances. By integrating the sub-micron simulation environment into a distributed FSI simulation we enable the study of previously unfeasible research questions involving numerous adhesive cells in microvessel networks such as cancer cell transport through the microcirculation.

Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve.

Computation of Fractional Flow Reserve (FFR) through computational fluid dynamics (CFD) is used to guide intervention and often uses a number of clinically-derived metrics, but these patient-specific data could be costly and difficult to obtain. Understanding which parameters can be approximated from population averages and which parameters need to be patient-specific is important and remains largely unexplored. In this study, we performed a global sensitivity study on two 1D models of FFR to identify the most influential patient parameters. Our results indicated that vessel compliance, cardiac cycle period, flow rate, density, viscosity, and elastic modulus contributed minimally to the variance in FFR and may be approximated from population averages. On the other hand, outlet resistance (i.e., microvascular resistance), stenosis degree, and percent stenosis length contributed the most to FFR computation and needed to be tuned to the patient of interest. Selective measuring of patient-specific parameters may significantly reduce costs and streamline the simulation pipeline without reducing accuracy.

An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions.

Coronary bifurcation lesions are a leading cause of Coronary Artery Disease (CAD). Despite its prevalence, coronary bifurcation lesions remain difficult to treat due to our incomplete understanding of how various features of lesion anatomy synergistically disrupt normal hemodynamic flow. In this work, we employ an interpretable machine learning algorithm, the Classification and Regression Tree (CART), to model the impact of these geometric features on local hemodynamic quantities. We generate a synthetic arterial database via computational fluid dynamic simulations and apply the CART approach to predict the time averaged wall shear stress (TAWSS) at two different locations within the cardiac vasculature. Our experimental results show that CART can estimate a simple, interpretable, yet accurately predictive nonlinear model of TAWSS as a function of such features.Clinical relevance- The fitted tree models have the potential to refine predictions of disturbed hemodynamic flow based on an individual's cardiac and lesion anatomy and consequently makes progress towards personalized treatment planning for CAD patients.

A data-driven approach to modeling cancer cell mechanics during microcirculatory transport.

In order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer's cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

Non-invasive characterization of complex coronary lesions.

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, [Formula: see text] long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements ([Formula: see text]). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, [Formula: see text]]. Higher ESS was observed in the complex lesion group ([Formula: see text] Pa) than in simple lesion group ([Formula: see text] Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.

Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma.

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide-the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.