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A 1D–0D–3D coupled model for simulating blood flow and transport processes in breast tissue
In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D–0D–3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D–0D–3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.
Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data
Three-dimensional, time-resolved blood flow measurement (4D-flow) is a powerful research and clinical tool, but improved resolution and scan times are needed. Therefore, this study aims to (1) present a postprocessing framework for optimization-driven simulation-based flow imaging, called 4D-flow High-resolution Imaging with a priori Knowledge Incorporating the Navier-Stokes equations and the discontinuous Galerkin method (4D-flow HIKING), (2) investigate the framework in synthetic tests, (3) perform phantom validation using laser particle imaging velocimetry, and (4) demonstrate the use of the framework in vivo. An optimizing computational fluid dynamics solver including adjoint-based optimization was developed to fit computational fluid dynamics solutions to 4D-flow data. Synthetic tests were performed in 2D, and phantom validation was performed with pulsatile flow. Reference velocity data were acquired using particle imaging velocimetry, and 4D-flow data were acquired at 1.5 T. In vivo testing was performed on intracranial arteries in a healthy volunteer at 7 T, with 2D flow as the reference. Results Synthetic tests showed low error (0.4%-0.7%). Phantom validation showed improved agreement with laser particle imaging velocimetry compared with input 4D-flow in the horizontal (mean -0.05 vs -1.11 cm/s, P < .001; SD 1.86 vs 4.26 cm/s, P < .001) and vertical directions (mean 0.05 vs -0.04 cm/s, P = .29; SD 1.36 vs 3.95 cm/s, P < .001). In vivo data show a reduction in flow rate error from 14% to 3.5%. Phantom and in vivo results from 4D-flow HIKING show promise for future applications with higher resolution, shorter scan times, and accurate quantification of physiological parameters.
Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease
Significance Microfluidics is an important in vitro platform to gain insights into mechanics of blood flow and mechanisms of pathophysiology of human diseases. Extraction of 3D fields in microfluidics with dense cell suspensions remains a formidable challenge. We present artificial-intelligence velocimetry (AIV) as a general platform to determine 3D flow fields and a microaneurysm-on-a-chip to simulate blood flow in microaneurysms in patients with diabetic retinopathy. AIV is built on physics-informed neural networks that integrate seamlessly 2D images from microfluidic experiments or in vivo observations with physical laws to estimate full 3D velocity and stress fields. AIV could be integrated into imaging technologies to automatically infer key hemodynamic metrics from in vivo and in vitro biomedical images.
Development and performance of a HemeLB GPU code for human-scale blood flow simulation
In recent years, it has become increasingly common for high performance computers (HPC) to possess some level of heterogeneous architecture - typically in the form of GPU accelerators. In some machines these are isolated within a dedicated partition, whilst in others they are integral to all compute nodes - often with multiple GPUs per node - and provide the majority of a machine's compute performance. In light of this trend, it is becoming essential that codes deployed on HPC are updated to execute on accelerator hardware. Here, in this paper, we introduce a GPU implementation of the 3D blood flow simulation code HemeLB that has been developed using CUDA C++. We demonstrate how taking advantage of NVIDIA GPU hardware can achieve significant performance improvements compared to the equivalent CPU only code on which it has been built whilst retaining the excellent strong scaling characteristics that have been repeatedly demonstrated by the CPU version. With HPC positioned on the brink of the exascale era, we use HemeLB as a motivation to provide a discussion on some of the challenges that many users will face when deploying their own applications on upcoming exascale machines.
Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator
Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a mechanical system that provides rapid and short-term support for patients with cardiac failure. In many patients, pulmonary function is also impaired, resulting in poorly-oxygenated cardiac outflow competing against well-oxygenated VA-ECMO outflow, a condition known as North-South syndrome. North-South syndrome is a primary concern because of its potential to cause cerebral hypoxia, which has a critical influence on neurological complications often seen in this patient population. In order to reduce ischemic neurological complications, it is important to understand how clinical decisions regarding VA-ECMO parameters influence blood oxygenation. In our work, we studied the impacts of flow rate and cannulation site on oxygenation using a one-dimensional (1D) model to simulate blood flow. Our model was initially tested by comparing blood flow results to those observed from experimental work in VA-ECMO patients. The 1D model was combined with a two-phase flow model to simulate oxygenation. Additionally, the influence of various other clinician-tunable parameters on oxygenation in the common carotid arteries (CCAs) were tested, including, blood viscosity, cannula position within the insertion artery, heart rate, and systemic vascular resistance (SVR), as well as geometrical changes such as arterial radius and length. Our results indicated that blood oxygenation to the brain strongly depended on the cannula insertion site and the VA-ECMO flow rate with a weaker but potentially significant dependence on arterial radius. During femoral cannulation, VA-ECMO flow rates greater than ~4.9 L/min were needed to perfuse the CCAs. However, axillary and central cannulation began to perfuse the CCAs at significantly lower flow (~1 L/min). These results may help explain the incidence of cerebral hypoxia in this patient population and the common need to change cannulation strategies during treatment to address this clinical problem. While this work describes patient-averaged results, determining these relationships between VA-ECMO parameters and cerebral hypoxia is an important step towards future work to develop patient-specific models that clinicians can use to improve outcomes.
Physics-constrained coupled neural differential equations for one dimensional blood flow modeling
Not Available
Pre-Symptomatic COVID Screening
Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre-symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.
Pre Symptomatic COVID Screening
Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.
Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo
Characterizing blood flow dynamics in vivo is critical to understanding the function of the vascular network under physiological and pathological conditions. Existing methods for hemodynamic imaging have insufficient spatial and temporal resolution to monitor blood flow at the cellular level in large blood vessels. By using an ultrafast line-scanning module based on free-space angular chirped enhanced delay, we achieved two-photon fluorescence imaging of cortical blood flow at 1,000 two-dimensional (2D) frames and 1,000,000 one-dimensional line scans per second in the awake mouse. This orders-of-magnitude increase in temporal resolution allowed us to measure cerebral blood flow at up to 49 mm/s and observe pulsatile blood flow at harmonics of heart rate. Directly visualizing red blood cell (RBC) flow through vessels down to >800 µm in depth, we characterized cortical layer–dependent flow velocity distributions of capillaries, obtained radial velocity profiles and kilohertz 2D velocity mapping of multifile blood flow, and performed RBC flux measurements from penetrating blood vessels.
Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks
Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient’s hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient’s vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics – pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.
X-ray fluorescence mapping of brain tissue reveals the profound extent of trace element dysregulation in stroke pathophysiology
Abstract The brain is a privileged organ with regard to its trace element composition and maintains a robust barrier system to sequester this specialized environment from the rest of the body and the vascular system. Stroke is caused by loss of adequate blood flow to a region of the brain. Without adequate blood flow ischaemic changes begin almost immediately, triggering an ischaemic cascade, characterized by ion dysregulation, loss of function, oxidative damage, cellular degradation, and breakdown of the barrier that helps maintain this environment. Ion dysregulation is a hallmark of stroke pathophysiology and we observe that most elements in the brain are dysregulated after stroke. X-ray fluorescence-based detection of physiological changes in the neurometallome after stroke reveals profound ion dysregulation within the lesion and surrounding tissue. Not only are most elements significantly dysregulated after stroke, but the level of dysregulation cannot be predicted from a cell-level description of dysregulation. X-ray fluorescence imaging reveals that the stroke lesion retains <25% of essential K+ after stroke, but this element is not concomitantly elevated elsewhere in the organ. Moreover, elements like Na+, Ca2+, and Cl− are vastly elevated above levels available in normal brain tissue (>400%, >200%, and >150%, respectively). We hypothesize that weakening of the blood–brain barrier after stroke allows elements to freely diffuse down their concentration gradient so that the stroke lesion is in equilibrium with blood (and the compartments containing brain interstitial fluid and cerebrospinal fluid). The change observed for the neurometallome likely has consequences for the potential to rescue infarcted tissue, but also presents specific targets for treatment.
Effect of Low Frequency Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Hyperbaric Oxygen (HBO) on Awakening of Coma Patients with Traumatic Brain Injury
Coma caused by craniocerebral injury is a common condition of neurosurgical acute injury. There is no specific method to promote awakening in a clinic. Early comprehensive treatment may be helpful to patients. The common methods are hyperbaric oxygen (HBO) and low-frequency repetitive transcranial magnetic stimulation (rTMS). However, the application effect and mechanism of rTMS combined with HBO on coma patients with traumatic brain injury need to be further studied. The brain stem auditory evoked potential (BAEP) is examined by the Kennedy coma recovery scale (CRS-R), the recovery of brain function and the state of consciousness are evaluated, and the therapeutic effect is evaluated by the Glasgow Coma Scale (GCS). Cerebrospinal fluid NE level, MCA blood flow velocity, and left brainstem and right brainstem auditory evoked potential are used to evaluate brain rehabilitation. RTMS combined with HBO could shorten the wake-up time, improve the wake-up rate, improve the GCS score and CRS-R score, shorten the brain wave latency time of the left and right brainstem, increase the NE level of cerebrospinal fluid, and decrease the blood flow velocity of MCA. RTMS combines with HBO can improve the nerve excitability of brain cells, reduce the disturbance of consciousness, promote the functional recovery of brain injury, and has a certain role in promoting the awakening of patients with traumatic brain injury coma.
JACC: Leveraging HPC Meta-Programming and Performance Portability with the Just-in-Time and LLVM-based Julia Language
We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.
Multi-GPU immersed boundary method hemodynamics simulations
Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs
Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.
Evaluating the Influence of Hemorheological Parameters on Circulating Tumor Cell Trajectory and Simulation Time
Extravasation of circulating tumor cells (CTCs) occurs primarily in the microvasculature, where flow and cell interactions significantly affect the blood rheology. Capturing cell trajectory at this scale requires the coupling of several interaction models, leading to increased computational cost that scales as more cells are added or the domain size is increased. In this work, we focus on micro-scale vessels and study the influence of certain hemorheological factors, including the presence of red blood cell aggregation, hematocrit level, microvessel size, and shear rate, on the trajectory of a circulating tumor cell. We determine which of the aforementioned factors significantly affect CTC motion and identify those which can potentially be disregarded, thus reducing simulation time. We measure the effect of these elements by studying the radial CTC movement and runtime at various combinations of these hemorheological parameters. To accurately capture blood flow dynamics and single cell movement, we perform high-fidelity hemodynamic simulations at a sub-micron resolution using our in-house fluid dynamics solver, HARVEY. We find that increasing hematocrit increases the likelihood of tumor cell margination, which is exacerbated by the presence of red blood cell aggregation. As microvessel diameter increases, there is no major CTC movement towards the wall; however, including aggregation causes the CTC to marginate quicker as the vessel size increases. Finally, as the shear rate is increased, the presence of aggregation has a diminished effect on tumor cell margination.
Influence of Blood Rheology and Turbulence Models in the Numerical Simulation of Aneurysms
An aneurysm is a vascular malformation that can be classified according to its location (cerebral, aortic) or shape (saccular, fusiform, and mycotic). Recently, the study of blood flow interaction with aneurysms has gained attention from physicians and engineers. Shear stresses, oscillatory shear index (OSI), gradient oscillatory number (GON), and residence time have been used as variables to describe the hemodynamics as well as the origin and evolution of aneurysms. However, the causes and hemodynamic conditions that promote their growth are still under debate. The present work presents numerical simulations of three types of aneurysms: two aortic and one cerebral. Simulation results showed that the blood rheology is not relevant for aortic aneurysms. However, for the cerebral aneurysm case, blood rheology could play a relevant role in the hemodynamics. The evaluated turbulence models showed equivalent results in both cases. Lastly, a simulation considering the fluid–structure interaction (FSI) showed that this phenomenon is the dominant factor for aneurysm simulation.