A reduced-order modeling for efficient design study of artificial valve in enlarged ventricular outflow tracts

A computational approach is proposed for efficient design study of a reducer stent to be percutaneously implanted in enlarged right ventricular outflow tracts (RVOT). The need for such a device is driven by the absence of bovine or artificial valves which could be implanted in these RVOT to replace the absent or incompetent native valve, as is often the case over time after Tetralogy of Fallot repair. Hemodynamics are simulated in the stented RVOT via a reduce order model based on proper orthogonal decomposition (POD), while the artificial valve is modeled as a thin resistive surface. The reduced order model is obtained from the numerical solution on a reference device configuration, then varying the geometrical parameters (diameter) for design purposes. To validate the approach, forces exerted on the valve and on the reducer are monitored, varying with geometrical parameters, and compared with the results of full CFD simulations. Such an approach could also be useful for uncertainty quantification

Group-wise Construction of Reduced Models for Understanding and Characterization of Pulmonary Blood Flows from Medical Images

International audience3D computational fluid dynamics (CFD) in patient-specific geometries provides complementary insights to clinical imaging, to better understand how heart disease, and the side effects of treating heart disease, affect and are affected by hemodynamics. This information can be useful in treatment planning for designing artificial devices that are subject to stress and pressure from blood flow. Yet, these simulations remain relatively costly within a clinical context. The aim of this work is to reduce the complexity of patient-specific simulations by combining image analysis, computational fluid dynamics and model order reduction techniques. The proposed method makes use of a reference geometry estimated as an average of the population, within an efficient statistical framework based on the currents representation of shapes. Snapshots of blood flow simulations performed in the reference geometry are used to build a POD (Proper Orthogonal Decomposition) basis, which can then be mapped on new patients to perform reduced order blood flow simulations with patient specific boundary conditions. This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts. The model reduction approach is further tested by performing patient simulations under exercise and varying degrees of pathophysiological conditions based on reduction of reference solutions (rest and medium backflow conditions respectively)

Computational Explorations in Biomedicine: Unraveling Molecular Dynamics for Cancer, Drug Delivery, and Biomolecular Insights using LAMMPS Simulations

With the rapid advancement of computational techniques, Molecular Dynamics (MD) simulations have emerged as powerful tools in biomedical research, enabling in-depth investigations of biological systems at the atomic level. Among the diverse range of simulation software available, LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) has gained significant recognition for its versatility, scalability, and extensive range of functionalities. This literature review aims to provide a comprehensive overview of the utilization of LAMMPS in the field of biomedical applications. This review begins by outlining the fundamental principles of MD simulations and highlighting the unique features of LAMMPS that make it suitable for biomedical research. Subsequently, a survey of the literature is conducted to identify key studies that have employed LAMMPS in various biomedical contexts, such as protein folding, drug design, biomaterials, and cellular processes. The reviewed studies demonstrate the remarkable contributions of LAMMPS in understanding the behavior of biological macromolecules, investigating drug-protein interactions, elucidating the mechanical properties of biomaterials, and studying cellular processes at the molecular level. Additionally, this review explores the integration of LAMMPS with other computational tools and experimental techniques, showcasing its potential for synergistic investigations that bridge the gap between theory and experiment. Moreover, this review discusses the challenges and limitations associated with using LAMMPS in biomedical simulations, including the parameterization of force fields, system size limitations, and computational efficiency. Strategies employed by researchers to mitigate these challenges are presented, along with potential future directions for enhancing LAMMPS capabilities in the biomedical field.Comment: 39 pages- 10 figure

Experimental Investigation of the Flow Dynamics in Models of Patient-Specific Aneurysms

This work investigates the complex flow dynamics in patient-specific compliant models of Abdominal Aortic Aneurysms (AAA) using time-resolved Particle Image Velocimetry (PIV). Scans of multiple planes were performed on three different models: a healthy aorta, a 4-cm saccular AAA, and a 7-cm fusiform AAA. We discuss the differences in flow patterns in patient-specific models compared to idealized models from previous work. We note that the curvature of the aorta upstream from the aneurysm, specific placement of the iliac arteries, and the overall symmetry of the aneurysm have important effects on flow structures, such as increasing transient effects, vortex formation, and wall impingement. Viscous energy dissipation rate (VEDr) was also evaluated as it has been previously identified as a potentially good metric to assess the severity of some vascular diseases. Finally, a modal analysis was performed on the velocity fields using Proper Orthogonal Decomposition (POD). The main modes obtained were inspected to identify the dominant structures, and the distribution of energy between the modes (Shannon entropy), and to create a reduced-order model of the flow. The results show that Shannon entropy was significantly different between the three models, suggesting that it can be a promising clinical parameter to evaluate the severity of AAAs

Statistical Fluid Dynamics

Modeling micrometric and nanometric suspensions remains a major issue. They help to model the mechanical, thermal, and electrical properties, among others, of the suspensions, and then of the resulting product, in a controlled way, when considered in material formation. In some cases, they can help to improve the energy transport performance. The optimal use of these products is based on an accurate prediction of the flow-induced properties of the suspensions and, consequently, of the resulting products and parts. The final properties of the resulting micro-structured fluid or solid are radically different from the simple mixing rule. In this book, we found numerous works addressing the description of these specific fluid behaviors

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Advanced Visualization and Intuitive User Interface Systems for Biomedical Applications

Modern scientific research produces data at rates that far outpace our ability to comprehend and analyze it. Such sources include medical imaging data and computer simulations, where technological advancements and spatiotemporal resolution generate increasing amounts of data from each scan or simulation. A bottleneck has developed whereby medical professionals and researchers are unable to fully use the advanced information available to them. By integrating computer science, computer graphics, artistic ability and medical expertise, scientific visualization of medical data has become a new field of study. The objective of this thesis is to develop two visualization systems that use advanced visualization, natural user interface technologies and the large amount of biomedical data available to produce results that are of clinical utility and overcome the data bottleneck that has developed. Computational Fluid Dynamics (CFD) is a tool used to study the quantities associated with the movement of blood by computer simulation. We developed methods of processing spatiotemporal CFD data and displaying it in stereoscopic 3D with the ability to spatially navigate through the data. We used this method with two sets of display hardware: a full-scale visualization environment and a small-scale desktop system. The advanced display and data navigation abilities provide the user with the means to better understand the relationship between the vessel\u27s form and function. Low-cost 3D, depth-sensing cameras capture and process user body motion to recognize motions and gestures. Such devices allow users to use hand motions as an intuitive interface to computer applications. We developed algorithms to process and prepare the biomedical and scientific data for use with a custom control application. The application interprets user gestures as commands to a visualization tool and allows the user to control the visualization of multi-dimensional data. The intuitive interface allows the user to control the visualization of data without manual contact with an interaction device. In developing these methods and software tools we have leveraged recent trends in advanced visualization and intuitive interfaces in order to efficiently visualize biomedical data in such a way that provides meaningful information that can be used to further appreciate it