The effect of geometrical configurations on flows in idealised and realistic vascular geometries

This thesis reports the use of computational fluid dynamics (CFD) to investigate geometrical effects on flows in idealised non-branching double curved geometries (Study A) and in realistic distal anastomoses where geometries have been determined in vivo using magnetic resonance imaging (Study B). The purpose of this research is to improve understanding of the effects of geometrical configurations, especially curvature and non-planarity, on steady flow in idealised non-branching double curved geometries typical of arteries such as the aortic arch, the right coronary artery or the femoral arteries and on pulsatile flow in realistic distal anastomosis geometries. It is explained that the further knowledge gained from these idealised geometries can be useful to understand flows in anatomically correct geometries in order to optimise the design of end-to-side bypass graft vessels in clinical surgery. In the Study A, three-dimensional computations of steady flows in planar and non-planar double bends with 8 = 0.25 (curvature ratio) at Reynolds numbers of 125 and 500 were performed using the Navier-Stokes solver called Nektar that is based on spectral/hp element methods. The numerical haemodynamics analysis is presented in terms of the various mechanical factors which primarily involve axial velocity, transverse flows, vorticity, coherent vertical structure and wall shear stress. From these results, we can anticipate the wall shear stress distributions and secondary flow patterns in various double bend geometries with different non-planarity at low Reynolds numbers. Non-planarity plays a significant role on the wall shear stress distribution and the mixing properties of flow in the double bends, both of which are believed to be important factors for the patency of bypass grafts. In the Study B, Three sets of MRI data from patients undergoing tunnelled or superficial femoral bypass surgery were processed to give input velocity waveforms and geometries. From the latter and using the same Navier Stokes solver, detailed patient-specific pulsatile haemodynamics were calculated. Wall shear stress and velocity are influenced by the anatomical geometry, and wall shear stress distributions in pulsatile flow are compared with those in steady flow. The correlation is discussed between the haemodynamics and the remodelling of the vessel following bypass surgery determined in follow-up studies carried out a few months or a few years later. The results suggest that better designs should be possible for bypass grafts and indicate how inflow conditions can affect the flow field. The findings imply that proper anastomotical configurations might, induce haemodynamics environments that may prevent cardiovascular disorders or delay the progression of vascular disease.Open acces

Studies of fluid flow in arterial bypass grafts by magnetic resonance imaging

This thesis is concerned primarily with the effects of graft geometry and flow pulsatility on the hemodynamics of arterial bypass grafts. Motivation for this work was the need of better understanding of the underlying flow related conditions that promote arterial bypass graft stenosis. The thesis describes techniques currently available for quantitative flow Magnetic Resonance Imaging (MRI). Methods to estimate and reduce measurement uncertainty in MRI phase contrast velocimetry are investigated. The MR phase-velocity mapping technique is calibrated in complex steady and transient flows by using highly resolved numerical computations as reference solutions. A novel splines based approximation method is introduced to model MRI velocity encoded phase. Applications of the proposed method to estimate wall shear stress and vorticity from MR phase velocity measurements are described and results from a series of validation studies are presented. The influence of out-of-plane geometry on the flow field in arterial bypass grafts is assessed by comparing two idealized model geometries: a planar and a non-planar configuration under steady and time-varying flow. Procedures for the acquisition of three-dimensional bypass graft geometries by MRI and their transformation to a com-puter-aided-design (CAD) representation are described. Stereolithographic replicas of bypass graft geometries (an in vivo extracted femoro-tibial distal anastomosis and an ex vivo imaged aorto-coronary distal anastomosis) and silicon negative models were fabricated for use in MRI flow studies. The effect of flow pulsatility on the flow features in the realistic aorto-coronary distal anastomosis model is investigated by comparing the flow field generated by a sinusoidal (single harmonic) and a physiological (multi-harmonic) waveform. The influence of the distal-proximal graft outflow division on the distal anastomotic sinus flow field in the femoro-tibial bypass graft model is examined. Finally, an account of the results from the comparative studies is given, and the physiological implications of the flow features of note are discussed

Recovery of the Shear Modulus and Residual Stress of Hyperelastic Soft Tissues by Inverse Spectral Techniques

Inverse spectral techniques are developed in this dissertation for recovering the shear modulus and residual stress of soft tissues. Shear modulus is one of several quantities for measuring the stiffness of a material, and hence estimating it accurately is an important factor in tissue characterization. Residual stress is a stress that can exist in a body in the absence of externally applied loads, and beneficial for biological growth and remodeling. It is a challenge to recover the two quantities in soft tissues both theoretically and experimentally. The current inverse spectral techniques recover the two unknowns invasively, and are theoretically based on a novel use of the intravascular ultrasound technology (IVUS) by obtaining several natural frequencies of the vessel wall material. As the IVUS is interrogating inside the artery, it produces small amplitude, high frequency time harmonic vibrations superimposed on the quasistatic deformation of the blood pressure pre-stressed and residually stressed artery. The arterial wall is idealized as a nonlinear isotropic cylindrical hyperelastic body for computational convenience. A boundary value problem is formulated for the response of the arterial wall within a specific class of quasistatic deformations reflexive of the response due to imposed blood pressures. Subsequently, a boundary value problem is developed from intravascular ultrasound interrogation generating small amplitude, high frequency time harmonic vibrations superimposed on the quasistatic finite deformations via an asymptotic construction of the solutions. This leads to a system of second order ordinary Sturm-Liouville problems (SLP) with the natural eigenfrequencies from IVUS implementation as eigenvalues of the SLP. They are then employed to reconstruct the shear modulus and residual stress in a nonlinear approach by inverse spectral techniques. The shear modulus is recovered by a multidimensional secant method (MSM). The MSM avoids computing the Jacobian matrix of the equations and is shown to be convenient for manipulation. Residual stress is recovered via an optimization approach (OA) instead of the traditional equation-solving method. The OA increases the robustness of the algorithms by overdetermination of the problem, and comprehensive tests are performed to guarantee the accuracy of the solution. Numerical examples are displayed to show the viability of these techniques

Meshless Hemodynamics Modeling And Evolutionary Shape Optimization Of Bypass Grafts Anastomoses

Objectives: The main objective of the current dissertation is to establish a formal shape optimization procedure for a given bypass grafts end-to-side distal anastomosis (ETSDA). The motivation behind this dissertation is that most of the previous ETSDA shape optimization research activities cited in the literature relied on direct optimization approaches that do not guaranty accurate optimization results. Three different ETSDA models are considered herein: The conventional, the Miller cuff, and the hood models. Materials and Methods: The ETSDA shape optimization is driven by three computational objects: a localized collocation meshless method (LCMM) solver, an automated geometry pre-processor, and a genetic-algorithm-based optimizer. The usage of the LCMM solver is very convenient to set an autonomous optimization mechanism for the ETSDA models. The task of the automated pre-processor is to randomly distribute solution points in the ETSDA geometries. The task of the optimized is the adjust the ETSDA geometries based on mitigation of the abnormal hemodynamics parameters. Results: The results reported in this dissertation entail the stabilization and validation of the LCMM solver in addition to the shape optimization of the considered ETSDA models. The LCMM stabilization results consists validating a custom-designed upwinding scheme on different one-dimensional and two-dimensional test cases. The LCMM validation is done for incompressible steady and unsteady flow applications in the ETSDA models. The ETSDA shape optimization include single-objective optimization results in steady flow situations and bi-objective optimization results in pulsatile flow situations. Conclusions: The LCMM solver provides verifiably accurate resolution of hemodynamics and is demonstrated to be third order accurate in a comparison to a benchmark analytical solution of the Navier-Stokes. The genetic-algorithm-based shape optimization approach proved to be very effective for the conventional and Miller cuff ETSDA models. The shape optimization results for those two models definitely suggest that the graft caliber should be maximized whereas the anastomotic angle and the cuff height (in the Miller cuff model) should be chosen following a compromise between the wall shear stress spatial and temporal gradients. The shape optimization of the hood ETSDA model did not prove to be advantageous, however it could be meaningful with the inclusion of the suture line cut length as an optimization parameter

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

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

Reduced Models for Optimal Control, Shape Optimization and Inverse Problems in Haemodynamics

The objective of this thesis is to develop reduced models for the numerical solution of optimal control, shape optimization and inverse problems. In all these cases suitable functionals of state variables have to be minimized. State variables are solutions of a partial differential equation (PDE), representing a constraint for the minimization problem. The solution of these problems induce large computational costs due to the numerical discretization of PDEs and to iterative procedures usually required by numerical optimization (many-query context). In order to reduce the computational complexity, we take advantage of the reduced basis (RB) approximation for parametrized PDEs, once the state problem has been reformulated in parametrized form. This method enables a rapid and reliable approximation of parametrized PDEs by constructing low-dimensional, problem-specific approximation spaces. In case of PDEs defined over domains of variable shapes (e.g. in shape optimization problems) we need to introduce suitable, low-dimensional shape parametrization techniques in order to tackle the geometrical complexity. Free-Form Deformations and Radial-Basis Functions techniques have been analyzed and successfully applied with this aim. We analyze the reduced framework built by coupling these tools and apply it to the solution of optimal control and shape optimization problems. Robust optimization problems under uncertain conditions are also taken into consideration. Moreover, both deterministic and Bayesian frameworks are set in order to tackle inverse identification problems. As state equations, we consider steady viscous flow problems described by Stokes or Navier-Stokes equations, for which we provide a detailed analysis and construction of RB approximation and a posteriori error estimation. Several numerical test cases are also illustrated to show efficacy and reliability of RB approximations. We exploit this general reduced framework to solve some optimization and inverse problems arising in haemodynamics. More specifically, we focus on the optimal design of cardiovascular prostheses, such as bypass grafts, and on inverse identification of pathological conditions or flow/shape features in realistic parametrized geometries, such as carotid artery bifurcations