Assessment of predicting blood flow and atherosclerosis in the aorta and renal arteries
Time: Fri 2020-08-28 14.00
Location: F3, Brinellvägen 8, 114 28 Stockholm, Stockholm (English)
Subject area: Engineering Mechanics
Doctoral student: Alexander Fuchs , Biomekanik
Opponent: Professor Elazer Edelman, Harvard-MIT, Biomedical Engineering Center, Boston, MA, USA
Supervisor: Lektor Lisa Prahl Wittberg, ; Professor Örjan Smedby, Medicinsk avbildning; Docent Chunliang Wang, Medicinsk avbildning; Professor Anders Persson, CMIV, Linköpings Universitet
Cardiovascular diseases (CVD) are the most common cause of death in large parts of the world. Atherosclerosis (AS) has a major part in most CVDs. AS is a slowly developingdisease which is dependent on multiple factors such as genetics and life style (food, smoking, and physical activities). AS is primarily a disease of the arterial wall and develops preferentially at certain locations (such as arterial branches and in certain vessels like thecoronary arteries). The close relation between AS sites and blood flow has been well established over the years. However, due to multi-factorial causes, there exist no early prognostic tools for identifying individuals that should be treated prophylactically or followed up. The underlying hypothesis of this thesis was to determine if it is possible to use bloodflow simulations of patient-specific cases in order to identify individuals with risk for developing AS. CT scans from patients with renal artery stenosis (RAS) were used to get the affected vessels geometry. Blood flow in original and “reconstructed” arteries were simulated. Commonly used wall shear stress (WSS) related indicators of AS were studied to assess their use as risk indicators for developing AS. Divergent results indicated urgent need to assess the impact ofsimulation related factors on results. Altogether, blood flow in the following vessels was studied: The whole aorta with branches from the aortic arch and the abdominal aorta, abdominal aorta as well as the renal arteries, and separately the thoracic aorta with the three main branching arteries from the aortic arch. The impact of geometrical reconstruction, employed boundary conditions (BCs), effects of flow-rate, heart-rate and models of blood viscosity as function of local hematocrit (red blood cell, RBC, concentration) and shear-rate were studied in some detail. In addition to common WSS-related indicators, we suggested the use of endothelial activation models as a further risk indicator. The simulations data was used to extract not only the WSS-related data but also the impact of flow-rate on the extent of retrograde flow in the aorta and close to its walls. The formation of helical motion and flow instabilities (which at high flow- and heart-rate lead to turbulence) was also considered.
A large number of simulations (more than 100) were carried out. These simulations assessed the use of flow-rate specified BCs, pressure based BCs or so called windkessel (WK) outlet BCs that simulate effects of peripheral arterial compliance. The results showed high sensitivity of the flow to BCs. For example, the deceleration phase of the flow-rate is more prone to flow instabilities (as also expressed in terms of multiple inflection points in the streamwise velocity profile) as well as leading to retrograde flow. In contrast, the acceleration phase leads to uni-directional and more stable flow. As WSS unsteadiness was found to be pro-AS, it was important to assess the effect flow-rate deceleration, under physiological and pathological conditions. Peaks of retrograde flow occur at local temporal minima in flow-rate. WK BCs require ad-hoc adjusted parameters and are therefore useful only when fully patient specific (i.e. all information is valid for a particular patient at a particular point of time) data is available. Helical flows which are considered as atheroprotective, are formed naturally, depending primarily on the geometry (due to the bends in the thoracic aorta). Helical flow was also observed in the major aortic branches. The helical motion is weaker during flow deceleration and diastole when it may locally also change direction. Most common existing blood viscosity models are based on hematocrit and shear-rate. These models show strong variation of blood (mixture) viscosity. With strong shear-rate blood viscosity is lowest and is almost constant. The impact of blood viscosity in terms of dissipation is counter balanced by the shear-rate; At low shear-rate the blood has larger viscosity and at high shear-rate it is the opposite. This effect and due to the temporal variations in the local flow conditions the effect of blood rheology on the WSS indicators is weak. Tracking of blood components and clot-models shows that the retrograde motion and the flow near branches may have so strong curvature that centrifugal force can become important. This effect may lead to the transport of a thrombus from the descending aorta back to the branches of the aortic arch and could cause embolic stroke. The latter results confirm clinical observation of the risk of stroke due to transport of emboli from the proximal part of the descending aorta upstream to the vessels branching from the aortic arch and which lead blood to the brain.
The main reasons for not being able to propose an early predictive tool for future developmentof AS are four-folded:
i. At present, the mechanisms behind AS are not adequately understood to enable to define aset of parameters that are sensitive and specific enough to be predictive of its development.
ii. The lack of accurate patient-specific data (BC:s) over the whole physiological “envelop”allows only limited number of flow simulations which may not be adequate for patientspecificpredictive purposes.
iii. The shortcomings of current models with respect to material properties of blood andarterial walls (for patient-specific space- and time-variations) are lacking.
iv. There is a need for better simulation data processing, i.e. tools that enable deducinggeneral predictive atherosclerotic parameters from a limited number of simulations, throughe.g. extending reduced modeling and/or deep learning.
The results do show, however, that blood flow simulations may produce very useful data thatenhances understanding of clinically observed processes such as explaining helical- andretrograde flows and the transport of blood components and emboli in larger arteries.