Multicompartmental poroelastic modelling for CSF production and circulation

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.This study proposes the implementation of a Multiple-Network Poroelastic Theory (MPET) model for the purpose of investigating in detail the transport of water within the cerebral environment. The advantage of using the MPET representation is that it accounts for fluid transport between CSF, brain parenchyma and cerebral blood. They key novelty in the model discussed in the present study is the amalgamation of anatomically accurate Choroid Plexus regions, with their individual feeding arteries. This model is used to demonstrate the impact of aqueductal stenosis and atresia of the Foramina of Luschka and Magendie on the cerebral ventricles. The possible implications of treating such a condition with the aid of endoscopic third ventriculostomy are investigated and discussed.This study is supported by the Research Councils UK cross council initiative led by EPSRC and contributed to by AHRC, ESRC, and MRC

Simple Patient-Based Transmantle Pressure and Shear Estimate From Cine Phase-Contrast MRI in Cerebral Aqueduct

From measurements of the oscillating flux of the cerebrospinal fluid (CSF) in the aqueduct of Sylvius, we elaborate a patient-based methodology for transmantle pressure (TRP) and shear evaluation. High-resolution anatomical magnetic resonance imaging first permits a precise 3-D anatomical digitalized reconstruction of the Sylvius’s aqueduct shape. From this, a very fast approximate numerical flow computation, nevertheless consistent with analytical predictions, is developed. Our approach includes the main contributions of inertial effects coming from the pulsatile flow and curvature effects associated with the aqueduct bending. Integrating the pressure along the aqueduct longitudinal center-line enables the total dynamic hydraulic admittances of the aqueduct to be evaluated, which is the pre-eminent contribution to the CSF pressure difference between the lateral ventricles and the subarachnoidal spaces also called the TRP. The application of the method to 20 healthy human patients validates the hypothesis of the proposed approach and provides a first database for normal aqueduct CSF flow. Finally, the implications of our results for modeling and evaluating intracranial cerebral pressure are discussed

Analysis of Growing Tumor on the Flow Velocity of Cerebrospinal Fluid in Human Brain Using Computational Modeling and Fluid-Structure Interaction

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles

Starling forces drive intracranial water exchange during normal and pathological states

Aim To quantify the exchange of water between cerebral compartments, specifically blood, tissue, perivascular pathways, and cerebrospinal fluid-filled spaces, on the basis of experimental data and to propose a dynamic global model of water flux through the entire brain to elucidate functionally relevant fluid exchange phenomena. Methods The mechanistic computer model to predict brain water shifts is discretized by cerebral compartments into nodes. Water and species flux is calculated between these nodes across a network of arcs driven by Hagen-Poiseuille flow (blood), Darcy flow (interstitial fluid transport), and Starling’s Law (transmembrane fluid exchange). Compartment compliance is accounted for using a pressurevolume relationship to enforce the Monro-Kellie doctrine. This nonlinear system of differential equations is solved implicitly using MATLAB software. Results The model predictions of intraventricular osmotic injection caused a pressure rise from 10 to 22 mmHg, followed by a taper to 14 mmHg over 100 minutes. The computational results are compared to experimental data with R2 = 0.929. Moreover, simulated osmotic therapy of systemic (blood) injection reduced intracranial pressure from 25 to 10 mmHg. The modeled volume and intracranial pressure changes following cerebral edema agree with experimental trends observed in animal models with R2 = 0.997. Conclusion The model successfully predicted time course and the efficacy of osmotic therapy for clearing cerebral edema. Furthermore, the mathematical model implicated the perivascular pathways as a possible conduit for water and solute exchange. This was a first step to quantify fluid exchange throughout the brain

Human intracranial pulsatility during the cardiac cycle: a computational modelling framework

Background Today’s availability of medical imaging and computational resources set the scene for high-fidelity computational modelling of brain biomechanics. The brain and its environment feature a dynamic and complex interplay between the tissue, blood, cerebrospinal fluid (CSF) and interstitial fluid (ISF). Here, we design a computational platform for modelling and simulation of intracranial dynamics, and assess the models’ validity in terms of clinically relevant indicators of brain pulsatility. Focusing on the dynamic interaction between tissue motion and ISF/CSF flow, we treat the pulsatile cerebral blood flow as a prescribed input of the model. Methods We develop finite element models of cardiac-induced fully coupled pulsatile CSF flow and tissue motion in the human brain environment. The three-dimensional model geometry is derived from magnetic resonance images (MRI) and features a high level of detail including the brain tissue, the ventricular system, and the cranial subarachnoid space (SAS). We model the brain parenchyma at the organ-scale as an elastic medium permeated by an extracellular fluid network and describe flow of CSF in the SAS and ventricles as viscous fluid movement. Representing vascular expansion during the cardiac cycle, a prescribed pulsatile net blood flow distributed over the brain parenchyma acts as the driver of motion. Additionally, we investigate the effect of model variations on a set of clinically relevant quantities of interest. Results Our model predicts a complex interplay between the CSF-filled spaces and poroelastic parenchyma in terms of ICP, CSF flow, and parenchymal displacements. Variations in the ICP are dominated by their temporal amplitude, but with small spatial variations in both the CSF-filled spaces and the parenchyma. Induced by ICP differences, we find substantial ventricular and cranial-spinal CSF flow, some flow in the cranial SAS, and small pulsatile ISF velocities in the brain parenchyma. Moreover, the model predicts a funnel-shaped deformation of parenchymal tissue in dorsal direction at the beginning of the cardiac cycle. Conclusions Our model accurately depicts the complex interplay of ICP, CSF flow and brain tissue movement and is well-aligned with clinical observations. It offers a qualitative and quantitative platform for detailed investigation of coupled intracranial dynamics and interplay, both under physiological and pathophysiological conditions.publishedVersio

Diagnostic and Therapeutic MEMS (Micro-Electro-Mechanical Systems) Devices for the Identification and Treatment of Human Disease

abstract: Early detection and treatment of disease is paramount for improving human health and wellness. Micro-scale devices promote new opportunities for the rapid, cost-effective, and accurate identification of altered biological states indicative of disease early-onset; these devices function at a scale more sensitive to numerous biological processes. The application of Micro-Electro-Mechanical Systems (MEMS) in biomedical settings has recently emerged and flourished over course of the last two decades, requiring a deep understanding of material biocompatibility, biosensing sensitively/selectively, biological constraints for artificial tissue/organ replacement, and the regulations in place to ensure device safety. Capitalizing on the inherent physical differences between cancerous and healthy cells, our ultra-thin silicone membrane enables earlier identification of bladder cancer—with a 70% recurrence rate. Building on this breakthrough, we have devised an array to multiplex this sample-analysis in real-time as well as expanding beyond bladder cancer. The introduction of new materials—with novel properties—to augment current and create innovative medical implants requires the careful analysis of material impact on cellular toxicity, mutagenicity, reactivity, and stability. Finally, the achievement of replacing defective biological systems with implanted artificial equivalents that must function within the same biological constraints, have consistent reliability, and ultimately show the promise of improving human health as demonstrated by our hydrogel check valve. The ongoing proliferation, expanding prevalence, and persistent improvement in MEMS devices through greater sensitivity, specificity, and integration with biological processes will undoubtedly bolster medical science with novel MEMS-based diagnostics and therapeutics.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Overcoming conventional modeling limitations using image- driven lattice-boltzmann method simulations for biophysical applications

The challenges involved in modeling biological systems are significant and push the boundaries of conventional modeling. This is because biological systems are distinctly complex, and their emergent properties are results of the interplay of numerous components/processes. Unfortunately, conventional modeling approaches are often limited by their inability to capture all these complexities. By using in vivo data derived from biomedical imaging, image-based modeling is able to overcome this limitation. In this work, a combination of imaging data with the Lattice-Boltzmann Method for computational fluid dynamics (CFD) is applied to tissue engineering and thrombogenesis. Using this approach, some of the unanswered questions in both application areas are resolved. In the first application, numerical differences between two types of boundary conditions: “wall boundary condition” (WBC) and “periodic boundary condition” (PBC), which are commonly utilized for approximating shear stresses in tissue engineering scaffold simulations is investigated. Surface stresses in 3D scaffold reconstructions, obtained from high resolution microcomputed tomography images are calculated for both boundary condition types and compared with the actual whole scaffold values via image-based CFD simulations. It is found that, both boundary conditions follow the same spatial surface stress patterns as the whole scaffold simulations. However, they under-predict the absolute stress values approximately by a factor of two. Moreover, it is found that the error grows with higher scaffold porosity. Additionally, it is found that the PBC always resulted in a lower error than the WBC. In a second tissue engineering study, the dependence of culture time on the distribution and magnitude of fluid shear in tissue scaffolds cultured under flow perfusion is investigated. In the study, constructs are destructively evaluated with assays for cellularity and calcium deposition, imaged using µCT and reconstructed for CFD simulations. It is found that both the shear stress distributions within scaffolds consistently increase with culture time and correlate with increasing levels of mineralized tissues within the scaffold constructs as seen in calcium deposition data and µCT reconstructions. In the thrombogenesis application, detailed analysis of time lapse microscopy images showing yielding of thrombi in live mouse microvasculature is performed. Using these images, image-based CFD modeling is performed to calculate the fluid-induced shear stresses imposed on the thrombi’s surfaces by the surrounding blood flow. From the results, estimates of the yield stress (A critical parameter for quantifying the extent to which thrombi material can resist deformation and breakage) are obtained for different blood vessels. Further, it is shown that the yielding observed in thrombi occurs mostly in the outer shell region while the inner core remains intact. This suggests that the core material is different from the shell. To that end, we propose an alternative mechanism of thrombogenesis which could help explain this difference. Overall, the findings from this work reveal that image-based modeling is a versatile approach which can be applied to different biomedical application areas while overcoming the difficulties associated with conventional modeling

Efecto de la geometría en las condiciones de flujo y esfuerzos en el acueducto de Silvio

96 páginasLa hidrocefalia es una enfermedad con una alta incidencia tanto en neonatos (1 en cada 1000 nacidos vivos) como en pacientes de edad avanzada. En el caso de los neonatos es usual que esta patología se genere de forma congénita por medio de un cerramiento de los conductos cerebrales. El Acueducto de Silvio (AS) es uno de los conductos que donde se genera hidrocefalia al obstruirse y lo que lleva a la disminución o cierre por completo el flujo de líquido cefalorraquídeo. Diversos estudios han simulado y hecho intentos para medir los patrones de flujo y presiones a través del AS, no obstante, las simulaciones realizadas por Computational Fluid Dynamics (CFD) y las mediciones realizadas con diferentes técnicas de imagenología resultan disímiles entre sí. Se ha especulado que uno de los principales factores que genera estas diferencias de la geometría del mismo AS, siendo un órgano relativamente pequeño, pero con características similares entre pacientes, la geometría puede tener cambios significativos de un paciente a otro. En este trabajo de grado se plantea realizar el estudio comparativo entre diferentes geometrías en el AS aplicando las mismas condiciones de frontera y simulando los patrones de presión, flujo y líneas de corriente por medio de técnicas computacionales de mecánica de fluidos computacional (CFD). Se partirá de imágenes médicas de resonancia magnética (MRI) en neonatos e infantes menores a 9 años para tomar diferentes patrones geométricos del AS. Los resultados muestran que la velocidad y esfuerzos cortantes, y por lo tanto el patrón de flujo, son sensibles a los cambios en la geometría del AS. También se encontró que no hay cambios significativos en la distribución de las presiones.PregradoIngeniero(a) Biomédico(a

Computational modelling of post-stroke brain injuries

The healthy functioning of the human brain depends on continuous and sufficient blood flow and metabolism. Severe interruptions in blood and oxygen supply can lead to energy impairment, and result in irreversible brain damage. In ischaemic stroke, a large reduction in blood supply leads to the breakdown of the blood-brain barrier (BBB) and reperfusion injury after reperfusion therapy. The increased BBB permeability disrupts the homeostasis of solutes between blood and interstitial fluid and therefore gives rise to fluid leakage from blood vessels into the interstitial space. As a result, brain injuries such as brain oedema and haemorrhagic transformation can occur. The simulation of the full brain has previously been performed based on mechanical and poroelastic models to study brain mechanics. In these studies, however, the modelling of post-stroke brain damage has not been considered and it is therefore necessary to propose new models for further investigations. This thesis further develops the simulation tools in the In Silico Clinical Trials for the Treatment of Acute Ischaemic Stroke (INSIST) project. I first propose a model for the simulation of osmotherapy, which is a common practice to relieve intracranial pressure (ICP) in brain oedema. The model is directly compared with clinical data for model validation. Furthermore, parameter sensitivity analysis is conducted to investigate the impact of various parameters, including shear modulus, tissue hydraulic permeability, and capillary vessel wall permeability, and to determine the effects of these physiological parameters on oedema development. Finally, different administration protocols are studied using the model and a near-optimal strategy for oedema treatment is proposed. Secondly, haemorrhagic transformation after stroke commonly occurs alongside oedema. Therefore, the model is further developed to include the growth of haematomas by simulating the flow of blood through the interstitial space. The simulation results are compared with clinical imaging to obtain the blood perfusion data and then utilised to investigate the effects of high blood glucose, blood pressure, etc. Thirdly, a study for the modelling of the contact mechanics of oedema in the brain is conducted. As the brain swells, the cerebrospinal fluid in the ventricle is compressed and drained to release pressure. A complex contact mechanics problem thus emerges which has not been previously solved. This model provides the first computational tool for the simulation of ventricle collapse and large brain deformation. Meanwhile, this study also focuses on the midline shift caused by brain swelling as it is a crucial criterion to determine the severity of oedema. Prediction curves of MLS-ICP relationships are proposed and compared with the clinical data. Finally, an AI-assisted model for the optimisation of osmotherapy is proposed. The data produced from the in-silico model are utilised to train a deep neural network for the generation of virtual patient groups with different blood-brain barrier damage levels and age. This provides an approach for the optimisation of treatment strategy for patient stratifications