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

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices. Graphical abstract: [Figure not available: see fulltext.]</p

Numerical Simulation of the Torus Wake Structure

This work presents three papers, those are mainly focused on the numerical simulations in order to scrutinize the force characteristics, wake structures and turbulence properties of the flow past a torus. The first paper compares the performance of URANS, LES, and IDDES turbulence models for simulating the flow around a torus with an aspect ratio of 3, at the Reynolds number of 9000, in terms of accuracy and cost-effectiveness. URANS fails to capture the turbulent nature of the flow, albeit it reliably predicts the mean flow. IDDES is found to be the optimal approach for this problem. It is less computationally expensive compared with LES, while the results provided are in accordance with those for LES and the documented experiments in the literature. In the second paper, an LES approach is carried out to study the effects of torus aspect ratio (AR) on the flow characteristics at the Reynolds number of 9000. Three aspect ratios of 2, 3 and 5 are investigated. For AR=2 and 3, the wake structure shows an asymmetric helical shedding pattern. For AR=5, a regular patterns of quasi-axisymmetric rings are observed downstream of the torus shedding alternately in the streamwise direction. It is followed by a paper examining the Reynolds number effects on the vortical structure and the shedding pattern of the flow behind a torus with an aspect ratio of 3, utilizing IDDES, a hybrid RANS-LES method. Three Reynolds numbers of 150, 1500 and 15000 are studied and compared. For Re=150, the wake is laminar and exhibits a large-scale hairpin structure shedding alternately from the opposite side of the centerline axis. For Re=1500 and 15000, the wake stands in the turbulent regime. The vortical structure has a helical shedding pattern that disperses chaotically around the centerline axis. The striking difference between the Re=1500 and 15000 is the onset of the vortex roll-up, that occurs closer to the torus leeward surface for the Re=15000

Computational inertial microfluidics:a review

Since the discovery of inertial focusing in 1961, numerous theories have been put forward to explain the migration of particles in inertial flows, but a complete understanding is still lacking. Recently, computational approaches have been utilized to obtain better insights into the underlying physics. In particular, fundamental aspects of particle focusing inside straight and curved microchannels have been explored in detail to determine the dependence of focusing behavior on particle size, channel shape, and flow Reynolds number. In this review, we differentiate between the models developed for inertial particle motion on the basis of whether they are semi-analytical, Navier-Stokes-based, or built on the lattice Boltzmann method. This review provides a blueprint for the consideration of numerical solutions for modeling of inertial particle motion, whether deformable or rigid, spherical or non-spherical, and whether suspended in Newtonian or non-Newtonian fluids. In each section, we provide the general equations used to solve particle motion, followed by a tutorial appendix and specified sections to engage the reader with details of the numerical studies. Finally, we address the challenges ahead in the modeling of inertial particle microfluidics for future investigators

Deformability-induced effects of red blood cells in flow

To ensure a proper health state in the human body, a steady transport of blood is necessary. As the main cellular constituent in the blood suspension, red blood cells (RBCs) are governing the physical properties of the entire blood flow. Remarkably, these RBCs can adapt their shape to the prevailing surrounding flow conditions, ultimately allowing them to pass through narrow capillaries smaller than their equilibrium diameter. However, several diseases such as diabetes mellitus or malaria are linked to an alteration of the deformability. In this work, we investigate the shapes of RBCs in microcapillary flow in vitro, culminating in a shape phase diagram of two distinct, hydrodynamically induced shapes, the croissant and the slipper. Due to the simplicity of the RBC structure, the obtained phase diagram leads to further insights into the complex interaction between deformable objects in general, such as vesicles, and the surrounding fluid. Furthermore, the phase diagram is highly correlated to the deformability of the RBCs and represents thus a cornerstone of a potential diagnostic tool to detect pathological blood parameters. To further promote this idea, we train a convolutional neural network (CNN) to classify the distinct RBC shapes. The benchmark of the CNN is validated by manual classification of the cellular shapes and yields very good performance. In the second part, we investigate an effect that is associated with the deformability of RBCs, the lingering phenomenon. Lingering events may occur at bifurcation apices and are characterized by a straddling of RBCs at an apex, which have been shown in silico to cause a piling up of subsequent RBCs. Here, we provide insight into the dynamics of such lingering events in vivo, which we consequently relate to the partitioning of RBCs at bifurcating vessels in the microvasculature. Specifically, the lingering of RBCs causes an increased intercellular distance to RBCs further downstream, and thus, a reduced hematocrit.Um die biologischen Funktionen im menschlichen Körper aufrechtzuerhalten ist eine stetige Versorgung mit Blut notwendig. Rote Blutzellen bilden den Hauptanteil aller zellulären Komponenten im Blut und beeinflussen somit maßgeblich dessen Fließeigenschaften. Eine bemerkenswerte Eigenschaft dieser roten Blutzellen ist ihre Deformierbarkeit, die es ihnen ermöglicht, ihre Form den vorherrschenden Strömungsbedingungen anzupassen und sogar durch Kapillaren zu strömen, deren Durchmesser kleiner ist als der Gleichgewichtsdurchmesser einer roten Blutzelle. Zahlreiche Erkrankungen wie beispielsweise Diabetes mellitus oder Malaria sind jedoch mit einer Veränderung dieser Deformierbarkeit verbunden. In der vorliegenden Arbeit untersuchen wir die hydrodynamisch induzierten Formen der roten Blutzellen in mikrokapillarer Strömung in vitro systematisch für verschiedene Fließgeschwindigkeiten. Aus diesen Daten erzeugen wir ein Phasendiagramm zweier charakteristischer auftretender Formen: dem Croissant und dem Slipper. Aufgrund der Einfachheit der Struktur der roten Blutzellen führt das erhaltene Phasendiagramm zu weiteren Erkenntnissen über die komplexe Interaktion zwischen deformierbaren Objekten im Allgemeinen, wie z.B. Vesikeln, und des sie umgebenden Fluids. Darüber hinaus ist das Phasendiagramm korreliert mit der Deformierbarkeit der Erythrozyten und stellt somit einen Eckpfeiler eines potentiellen Diagnosewerkzeugs zur Erkennung pathologischer Blutparameter dar. Um diese Idee weiter voranzutreiben, trainieren wir ein künstliches neuronales Netz, um die auftretenden Formen der Erythrozyten zu klassifizieren. Die Ausgabe dieses künstlichen neuronalen Netzes wird durch manuelle Klassifizierung der Zellformen validiert und weist eine sehr hohe Übereinstimmung mit dieser manuellen Klassifikation auf. Im zweiten Teil der Arbeit untersuchen wir einen Effekt, der sich direkt aus der Deformierbarkeit der roten Blutzellen ergibt, das Lingering-Phänomen. Diese Lingering-Ereignisse können an Bifurkationsscheiteln zweier benachbarter Kapillaren auftreten und sind durch ein längeres Verweilen von Erythrozyten an einem Scheitelpunkt gekennzeichnet. In Simulationen hat sich gezeigt, dass diese Dynamik eine Anhäufung von nachfolgenden roten Blutzellen verursacht. Wir analysieren die Dynamik solcher Verweilereignisse in vivo, die wir folglich mit der Aufteilung von Erythrozyten an sich gabelnden Gefäßen in der Mikrovaskulatur in Verbindung bringen. Insbesondere verursacht das Verweilen von Erythrozyten einen erhöhten interzellulären Abstand zu weiter stromabwärts liegenden Erythrozyten und damit einen reduzierten Hämatokrit

Jet Mixing Enhancement by High Amplitude Pulse Fluidic Actuation

Turbulent mixing enhancement has received a great deal of attention in the fluid mechanics community in the last few decades. Generally speaking, mixing enhancement involves the increased dispersion of the fluid that makes up a flow. The current work focuses on mixing enhancement of an axisymmetric jet via high amplitude fluidic pulses applied at the nozzle exit with high aspect ratio actuator nozzles. The work consists of small scale clean jet experiments, small scale micro-turbine engine experiments, and full scale laboratory simulated core exhaust experiments using actuators designed to fit within the engine nacelle of a full scale aircraft. The small scale clean jet experiments show that mixing enhancement compared to the unforced case is likely due to a combination of mechanisms. The first mechanism is the growth of shear layer instabilities, similar to that which occurs with an acoustically excited jet except that, in this case, the forcing is highly nonlinear. The result of the instability is a frequency bucket with an optimal forcing frequency. The second mechanism is the generation of counter rotating vortex pairs similar to those generated by mechanical tabs. The penetration depth determines the extent to which this mechanism acts. The importance of this mechanism is therefore a function of the pulsing amplitude. The key mixing parameters were found to be the actuator to jet momentum ratio (amplitude) and the pulsing frequency, where the optimal frequency depends on the amplitude. The importance of phase, offset, duty cycle, and geometric configuration were also explored. The experiments on the jet engine and full scale simulated core nozzle demonstrated that pulse fluidic mixing enhancement was effective on realistic flows. The same parameters that were important for the cleaner small scale experiments were found to be important for the more realistic cases as well. This suggests that the same mixing mechanisms are at work. Additional work was done to optimize, in real time, mixing on the small jet engine using an evolution strategy.Ph.D.Committee Chair: David Parekh; Committee Member: Ari Glezer; Committee Member: Jeff Jagoda; Committee Member: Richard Gaeta; Committee Member: Samuel Shelto

Modeling Oxygen Transport in Three-Dimensional Capillary Networks

The purpose of this thesis was to examine how the use of real 3-dimensional (3D) capillary network geometries affect models of oxygen transport to tissue. Software was developed to reconstruct microvascular geometry in 3D from intravital video. Characterization of 3D reconstructions demonstrated that capillary density, length and capillary diameter were consistent with previous findings. Using reconstructed capillary networks a strategy was devised that utilized red blood cell (RBC) supply rate (SR) as a metric for flow modeling. Applying the RBC SR based flow model on baseline and perturbed flow conditions demonstrated that RBC SR is a major determinant of oxygen delivery that is insensitive to changes in flow distribution. The resulting flow solutions were used for comparing oxygen transport in 3D networks and synthetic parallel arrays. A variety of physiological conditions were simulated and it was determined that parallel arrays resulted in oxygen transport solutions with higher mean PO2 due to homogeneous distribution of vessels in the volume. Lastly, to investigate oxygen transport in a complex pathology a model of sepsis was used to investigate how incremental perfusion loss, consumption increase and change in RBC SR affect oxygen delivery. It was shown that perfusion loss did not markedly impair oxygen delivery provided that RBC SR was maintained. These results have improved our understanding of oxygen transport to tissue in normal and diseased conditions; the use of reconstructed networks and measurements of blood flow & oxygen saturation in computer models provides different solutions than those using statistical averages and synthetic networks