Multiscale Modeling of Hemodynamics in Human Vessel Network and Its Applications in Cerebral Aneurysms

Three-dimensional (3D) simulation of patient-specific morphological models has been widely used to provide the hemodynamic information of individual patients, such as wall shear stress (WSS), oscillatory shear index (OSI), and flow patterns, etc. Since patient-specific morphological segment was only restricted locally, boundary conditions (BCs) are required to implement the CFD simulation. Direct measurements of the flow and pressure waveforms were often required as input BCs for 3D CFD simulations of patient-specific models. However, as the morphology develops, the feedback from this topological deformation may lead to BCs being altered, and hence without this feedback, the flow characteristics of the morphology are only computed locally. A one-dimensional (1D) numerical model containing the entire human vessel network has been proposed to compute the global hemodynamics. In the meantime, experimental studies of blood flow in the patient-specific modeling of the circle of Willies (CoW) was conducted. The flow and pressure waveforms were quantified to validate the accuracy of the pure 1D model. This 1D model will be coupled with a 3D morphological model to account for the effects of the altered BCs. The proposed 1D-3D multi-scale modeling approach investigates how the global hemodynamic changes can be induced by the local morphological effects, and in consequence, may further result in altering of BCs to interfere with the solution of the 3D simulation. Validation of the proposed multi-scale model has also been made by comparing the solution of the flow rate and pressure waveforms with the experimental data and 3D numerical simulations reported in the literature. Moreover, the multi-scale model is extended to study a patient-specific cerebral aneurysm and a stenosis model. The proposed multi-scale model can be used as an alternative to current approaches to study intracranial vascular diseases such as an aneurysm, stenosis, and combined cases

In-silico clinical trials for assessment of intracranial flow diverters

In-silico trials refer to pre-clinical trials performed, entirely or in part, using individualised computer models that simulate some aspect of drug effect, medical device, or clinical intervention. Such virtual trials reduce and optimise animal and clinical trials, and enable exploring a wider range of anatomies and physiologies. In the context of endovascular treatment of intracranial aneurysms, in-silico trials can be used to evaluate the effectiveness of endovascular devices over virtual populations of patients with different aneurysm morphologies and physiologies. However, this requires (i) a virtual endovascular treatment model to evaluate device performance based on a reliable performance indicator, (ii) models that represent intra- and inter-subject variations of a virtual population, and (iii) creation of cost-effective and fully-automatic workflows to enable a large number of simulations at a reasonable computational cost and time. Flow-diverting stents have been proven safe and effective in the treatment of large wide-necked intracranial aneurysms. The presented thesis aims to provide the ingredient models of a workflow for in-silico trials of flow-diverting stents and to enhance the general knowledge of how the ingredient models can be streamlined and accelerated to allow large-scale trials. This work contributed to the following aspects: 1) To understand the key ingredient models of a virtual treatment workflow for evaluation of the flow-diverter performance. 2) To understand the effect of input uncertainty and variability on the workflow outputs, 3) To develop generative statistical models that describe variability in internal carotid artery flow waveforms, and investigate the effect of uncertainties on quantification of aneurysmal wall shear stress, 4) As part of a metric to evaluate success of flow diversion, to develop and validate a thrombosis model to assess FD-induced clot stability, and 5) To understand how a fully-automatic aneurysm flow modelling workflow can be built and how computationally inexpensive models can reduce the computational costs

Lattice-Boltzmann interactive blood flow simulation pipeline.

PURPOSE:Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. METHODS:We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. RESULTS:The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. CONCLUSION:A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information

Mathematical modeling of thrombus formation in idealized models of aortic dissection: Initial findings and potential applications

Aortic dissection is a major aortic catastrophe with a high morbidity and mortality risk caused by the formation of a tear in the aortic wall. The development of a second blood filled region defined as the “false lumen” causes highly disturbed flow patterns and creates local hemodynamic conditions likely to promote the formation of thrombus in the false lumen. Previous research has shown that patient prognosis is influenced by the level of thrombosis in the false lumen, with false lumen patency and partial thrombosis being associated with late complications and complete thrombosis of the false lumen having beneficial effects on patient outcomes. In this paper, a new hemodynamics-based model is proposed to predict the formation of thrombus in Type B dissection. Shear rates, fluid residence time, and platelet distribution are employed to evaluate the likelihood for thrombosis and to simulate the growth of thrombus and its effects on blood flow over time. The model is applied to different idealized aortic dissections to investigate the effect of geometric features on thrombus formation. Our results are in qualitative agreement with in-vivo observations, and show the potential applicability of such a modeling approach to predict the progression of aortic dissection in anatomically realistic geometries

Computational fluid dynamics indicators to improve cardiovascular pathologies

In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians. The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve. Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies. We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field. It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version

Multi-time-lag PIV analysis of steady and pulsatile flows in a sidewall aneurysm

The effect of inflow waveform on the hemodynamics of a real-size idealized sidewall intracranial aneurysm (IA) model was investigated using particle imaging velocimetry (PIV). For this purpose, we implemented an error analysis based on several PIV measurements with different time lags to ensure high precision of velocity fields measured in both the IA and the parent artery. The relative error measured in the main part of the circulating volume was <1% despite the three orders of magnitude difference of parent artery and IA dome velocities. Moreover, important features involved in IA evolution were potentially emphasized from the qualitative and quantitative flow pattern comparison resulting from steady and unsteady inflows. In particular, the flow transfer in IA and the vortical structure were significantly modified when increasing the number of harmonics for a typical physiological flow, in comparison with quasi-steady conditions

Transient Cardiovascular Hemodynamics In A Patient-Specific Arterial System

The ultimate goal of the present study is to aid in the development of tools to assist in the treatment of cardiovascular disease. Gaining an understanding of hemodynamic parameters for medical implants allow clinicians to have some patient-specific proposals for intervention planning. In the present study a full cardiovascular experimental phantom and digital phantom (CFD model) was fabricated to study: (1) the effects of local hemodynamics on global hemodynamics, (2) the effects of transition from bed-rest to upright position, and (3) transport of dye (drug delivery) in the arterial system. Computational three dimensional (3-D) models (designs A, B, and C) stents were also developed to study the effects of stent design on hemodynamic flow and the effects of drug deposition into the arterial wall. The experimental phantom used in the present study is the first system reported in literature to be used for hemodynamic assessment in static and orthostatic posture changes. Both the digital and experimental phantom proved to provide different magnitudes of wall shear and normal stresses in sections where previous studies have only analyzed single arteries. The dye mass concentration study for the digital and experimental cardiovascular phantom proved to be useful as a surrogate for medical drug dispersion. The dye mass concentration provided information such as transition time and drug trajectory paths. For the stent design CFD studies, hemodynamic results (wall shear stress (WSS), normal stress, and vorticity) were assessed to determine if simplified stented geometries can be used as a surrogate for patient-specific geometries and the role of stent design on flow. Substantial differences in hemodynamic parameters were found to exist which confirms the need for patient-specific modeling. For drug eluting stent studies, the total deposition time for the drug into the arterial wall was approximately 3.5 months