MECHANICAL CHARACTERIZATION OF PORCINE AORTA USING MAGNETIC RESONANCE IMAGING

Determination of aortic mechanical properties in a non-invasive way would be an important step in predicting the onset and development of one of the most fatal degenerative cardiovascular diseases: abdominal aortic aneurysm(AAA). The approach presented in this work to achieve this goal couples Magnetic Resonance Imaging (MRI) with Finite Element (FE) analysis to define a model of aortic mechanical behaviour. In particular, the aortic fibrous structure was analysed using Diffusion Tensor MRI, the results of which showed that fibres could be tracked in the aortic tissue, and that their angles measured ( 15◦) are in accordance with the angles reported in literature. DTI was also applied to a frozen aorta, where the structural parameters obtained were different from those for fresh tissue thus indicating the potential of DTI to measure damage in aortic tissue. MRI was also used for characterization of aortic tissue deformation, using Phase Contrast MRI (PC MRI). With this technique circumferential strains were measured in an aorta, which on average ranged between 0.95-4.7%, in accordance with the range found in vivo from literature. A mechanical constitutive model was implemented, initially based on the structural information from DTI and uniaxial test data, in a finite element (FE) model. Strains estimated in the model under applied physiological pressure were compared with the strains measured using PC MRI. Material parameters of the constitutive model were changed iteratively until the strains matched, thus obtaining the material constants necessary to characterize the behaviour of the aorta non-invasively. This thesis clearly demonstrates the feasibility of a novel approach to mechanical characterization of aortas, based on the use of innovative MRI techniques. Moreover, the application of DTI to both fresh and frozen tissue, which clearly identified differences in the tissues at the fibre level, demonstrates the potential of DTI as a diagnostic tool for degenerative arterial diseases such as AAAs

Computational mechanics in pediatric medicine : an overview

From foetuses, to newborns, to children and adolescents there is a large number of pediatric patients in need of computational strategies to improve the treatment of their conditions. In fact, the pediatric age is characterized by very rapid and sudden changes, which make it extremely difficult to test standardized paradigms of care. In turn, this makes pediatric treatments highly personalized, especially when it comes to surgeries and prosthetics. While on the one hand there is the difficulty of conducting wide clinical trials on large patient populations for each stage of growth and development, especially when it comes to rare congenital disease; on the other hand there is the need to treat these conditions as soon as possible, sometimes even in the womb, to ensure a good quality of life in these subjects. The growing use of computational mechanics as patient-specific predictive tools for adult treatments has spurred interest in engineers, researchers and physicians for the use of computational methods in predicting the outcome of highly personalized pediatric treatments, for which the need of prognostic tools is high and vital. The translation of established computational methods used in adults to pediatric patients has its own challenges, from the lack of high-quality images, as children are often spared CT-scans and X-rays, to the need of an accurate predictions in very rapid times. In this minisymposium we aim to explore the state of the art of this novel, interdisciplinary area of application of computational mechanics, continuing the success of a similar minisymposium in WCCM18. Therefore, it will include a review of the computational strategies applied to pediatric care: from fluid dynamic models applied to the identification of the best surgeries to correct congenital heart defect, to biomechanical computational approaches to assess growth in children, to numerical models of fetal development, to simulations of pediatric medical device treatments. The outcome of this minisymposium will be a productive gathering of minds, where experts from different areas of pediatric computational mechanics will gather together and exchange thoughts on strategies to overcome the challenges of this area of study and ideas to further the application of computational mechanics as a clinical tool for pediatric medicine

Multi-parameter computational model of flow mediated dilation with fluid-structure interaction coupled with lumped parameter approaches

Flow-mediated dilation (FMD) is a valuable non-invasive clinical assessment of endothelial dysfunction, a key indicator of atherosclerosis. The progression of atherosclerosis from paediatric ages can lead to the manifestation of cardiovascular diseases (CVDs) in later life. Hence, early lesion detection in younger years using FMD will heavily support timely disease assessment and treatment. In FMD, the diameter of the brachial artery is measured ultrasonically before, during, and after the inflation of a cuff applied to the lower arm of a subject. The cuff is placed distal to the ultrasound probe to capture predominantly endothelium-dependent vasodilation [1].The process induces a period of reactive hyperaemia, causing the brachial artery to vasodilate, due to an increase in shear stress that results in release of nitric oxide. The brachial artery ‘speak dilation is used for calculating the percentage difference between peak and baseline diameter (FMD percentage).Modelling FMD computationally offers a non-invasive assessment of vascular health and haemodynamic parameters

Blood flow in an intracranial aneurysmal artery with a dual-layer stent

Intracranial aneurysms are focal dilatations of arteries in the cerebral vasculature that require surgical intervention once detected. Dual-layer flow-diverting stents are the latest innovation intended for the endovascular treatment of intracranial aneurysms. A fine-mesh device, comprised of a low porosity inner stent and a high porosity outer stent, is implanted within the parent artery bearing the aneurysm. The inner layer is designed to alter the haemodynamics of both the aneurysm and parent vessel, and is therefore the active flow-diverting element, while the outer layer ensures the patency of nearby perforating arteries 1,2 . The FRED (Flow - Redirection Endoluminal Device) stent is the first device of its kind, with clinical approval in European and international markets. Several studies investigating the efficacy of the FRED stent have found its design to be safe and effective in the treatment of difficult-to-treat or otherwise untreatable intracranial aneurysms 2, 3. However, clinical radiological post-procedural data is limited. As of January 2018, only 370 patients across ten studies reported the efficacy of the FRED device, with primarily saccular and small aneurysms being treated. This work is the first detailed computational fluid dynamic analysis of blood flow in a section of an intracranial aneurysmal artery after treatment with a dual-layer stent. In particular, two-dimensional computational models implemented a virtual bench test to investigate local haemodynamic changes in part of the treated vessel. Preliminary results considers teady flow conditions, based on a parabolic inlet profile, for the solution of the incompressible Newtonian Navier-Stokes equations. Computational fluid dynamic analysis focussed on the variance of flow parameters, including velocity, pressure, vorticity, and wall shear stress, for three dual-layer stent models: a baseline model with the same number of total wires as the FRED stent on both layers; an alternative model with half the number of wires in the outer layer; and a second alternative model with half the number of wires in the inner layer 4. The most favourable flow behaviour in the aneurysmal artery was observed for the design in which the outer layer has half the number of wires than the inner layer. For all designs, the radial clearance between the two layers was seen to reduce the velocity of the surround ing blood flow to 98% of the blood flowing at the vessel centre. This research provides an aid to quantitatively estimate the flow of blood in a dual-layer treated vessel and will facilitate the optimisation of such devices

Immersed boundary-finite element model of fluid-structure interaction in the aortic root

It has long been recognized that aortic root elasticity helps to ensure efficient aortic valve closure, but our understanding of the functional importance of the elasticity and geometry of the aortic root continues to evolve as increasingly detailed in vivo imaging data become available. Herein, we describe fluid-structure interaction models of the aortic root, including the aortic valve leaflets, the sinuses of Valsalva, the aortic annulus, and the sinotubular junction, that employ a version of Peskin's immersed boundary (IB) method with a finite element (FE) description of the structural elasticity. We develop both an idealized model of the root with three-fold symmetry of the aortic sinuses and valve leaflets, and a more realistic model that accounts for the differences in the sizes of the left, right, and noncoronary sinuses and corresponding valve cusps. As in earlier work, we use fiber-based models of the valve leaflets, but this study extends earlier IB models of the aortic root by employing incompressible hyperelastic models of the mechanics of the sinuses and ascending aorta using a constitutive law fit to experimental data from human aortic root tissue. In vivo pressure loading is accounted for by a backwards displacement method that determines the unloaded configurations of the root models. Our models yield realistic cardiac output at physiological pressures, with low transvalvular pressure differences during forward flow, minimal regurgitation during valve closure, and realistic pressure loads when the valve is closed during diastole. Further, results from high-resolution computations demonstrate that IB models of the aortic valve are able to produce essentially grid-converged dynamics at practical grid spacings for the high-Reynolds number flows of the aortic root

Imaging Arterial Fibres Using Diffusion Tensor Imaging—Feasibility Study and Preliminary Results

<p>Abstract</p> <p>MR diffusion tensor imaging (DTI) was used to analyze the fibrous structure of aortic tissue. A fresh porcine aorta was imaged at 7T using a spin echo sequence with the following parameters: matrix 128 <inline-formula> <graphic file="1687-6180-2010-904091-i1.gif"/></inline-formula> 128 pixel; slice thickness 0.5&#8201;mm; interslice spacing 0.1&#8201;mm; number of slices 16; echo time 20.3&#8201;s; field of view 28&#8201;mm <inline-formula> <graphic file="1687-6180-2010-904091-i2.gif"/></inline-formula> 28&#8201;mm. Eigenvectors from the diffusion tensor images were calculated for the central image slice and the averaged tensors and the eigenvector corresponding to the largest eigenvalue showed two distinct angles corresponding to near <inline-formula> <graphic file="1687-6180-2010-904091-i3.gif"/></inline-formula> and <inline-formula> <graphic file="1687-6180-2010-904091-i4.gif"/></inline-formula> to the transverse plane of the aorta. Fibre tractography within the aortic volume imaged confirmed that fibre angles were oriented helically with lead angles of <inline-formula> <graphic file="1687-6180-2010-904091-i5.gif"/></inline-formula> and <inline-formula> <graphic file="1687-6180-2010-904091-i6.gif"/></inline-formula>. The findings correspond to current histological and microscopy data on the fibrous structure of aortic tissue, and therefore the eigenvector maps and fibre tractography appear to reflect the alignment of the fibers in the aorta. In view of current efforts to develop noninvasive diagnostic tools for cardiovascular diseases, DTI may offer a technique to assess the structural properties of arterial tissue and hence any changes or degradation in arterial tissue.</p

Imaging Arterial Fibres Using Diffusion Tensor Imaging&#x02014;Feasibility Study and Preliminary Results

MR diffusion tensor imaging (DTI) was used to analyze the fibrous structure of aortic tissue. A fresh porcine aorta was imaged at 7T using a spin echo sequence with the following parameters: matrix 128 &#x00D7; 128 pixel; slice thickness 0.5&#x2009;mm; interslice spacing 0.1&#x2009;mm; number of slices 16; echo time 20.3&#x2009;s; field of view 28&#x2009;mm &#x00D7; 28&#x2009;mm. Eigenvectors from the diffusion tensor images were calculated for the central image slice and the averaged tensors and the eigenvector corresponding to the largest eigenvalue showed two distinct angles corresponding to near 0&#x2218; and 180&#x2218; to the transverse plane of the aorta. Fibre tractography within the aortic volume imaged confirmed that fibre angles were oriented helically with lead angles of 15&#x00B1;2.5&#x2218; and 175&#x00B1;2.5&#x2218;. The findings correspond to current histological and microscopy data on the fibrous structure of aortic tissue, and therefore the eigenvector maps and fibre tractography appear to reflect the alignment of the fibers in the aorta. In view of current efforts to develop noninvasive diagnostic tools for cardiovascular diseases, DTI may offer a technique to assess the structural properties of arterial tissue and hence any changes or degradation in arterial tissue

Evaluation of a Validation Method for MR Imaging-Based Motion Tracking Using Image Simulation

Magnetic Resonance (MR) imaging-based motion and deformation tracking techniques combined with finite element (FE) analysis are a powerful method for soft tissue constitutive model parameter identification. However, deriving deformation data from MR images is complex and generally requires validation. In this paper a validation method is presented based on a silicone gel phantom containing contrasting spherical markers. Tracking of these markers provides a direct measure of deformation. Validation of in vivo medical imaging techniques is often challenging due to the lack of appropriate reference data and the validation method may lack an appropriate reference. This paper evaluates a validation method using simulated MR image data. This provided an appropriate reference and allowed different error sources to be studied independently and allowed evaluation of the method for various signal-to-noise ratios (SNRs). The geometric bias error was between 0&#8211;5.560&#x00D7;10&#x2212;3 voxels while the noisy magnitude MR image simulations demonstrated errors under 0.1161 voxels (SNR: 5&#8211;35)