(H-286) Patient-specific Computational Modeling of Cerebral Blood Flow in Adults with Sickle Cell Disease

Introduction:
Sickle cell disease (SCD) is one of the most common genetic disorders affecting red blood cells and it impacts 100,000 people in the US and 20 million people globally1. Some patients with SCD suffer from clinical manifestations of neurovascular occlusions such as stroke, ischemia and moyamoya disease,2 but the etiology behind the development of these vascular occlusions remains unclear. Research on SCD has mainly focused on pediatric populations, but advancements in research and technology have led to longer life expectancy for patients with SCD, which highlights the need for more comprehensive studies focusing on adults.


Patients with SCD exhibit elevated velocities in arteries in the Circle of Willis (CoW), which has been correlated with an increased risk of stroke3. The CoW consists of blood vessels that distribute blood to different parts of the brain and functions as a collateral circulation when a distal vessel is occluded. To better understand the hemodynamic differences in the CoW associated with SCD, computational fluid dynamics (CFD) can be a useful tool to simulate patient-specific conditions and predict changes in blood flow4. The objective of this study is to use CFD to provide preliminary insight into how variations in wall shear stress (WSS), pressure, and velocity impact the likelihood of occlusive events in cerebral arteries and predict disease severity.



Materials and Methods::
Time of flight (TOF) images from a 51-year old female healthy control and a 44-year old female patient with SCD were obtained from a previous study to visualize blood vessels in the brain. TOF images were acquired using a 7T Siemens Magnetom MRI scanner with a customized 1st generation Tic Tac Toe radiofrequency (RF) head coil system that has 16-transmit channels and 32-receive channels5,6,7,8. The TOF were post-processed using bias correction. SimVascular, an open-source cardiovascular modeling and simulation software, was used to create 3D models of the CoW and to perform blood flow simulations using the finite element method6. To reconstruct the 3D model, the vessels were tracked on the MRI image to create a centerline path. 2D segmentations were created using the path lines of the vessels as a guide. Lastly, a 3D model was reconstructed in SimVascular and a tetrahedral finite element mesh was generated. The simulations were run using a model assuming 3 in-flows and 6 out-flows, with blood assumed to be a Newtonian fluid (Figure 1). At the inlets, the boundary condition (BC) used was a prescribed velocity waveform for a healthy control and patient with SCD at the basilar artery and internal carotid arteries (ICA)9. At the outlets, resistance was tuned to match the participant’s mean arterial pressure and BCs were specified using the systolic blood pressure of the participant from the electronic medical record, the cross-sectional area of the blood vessels and the velocity values in the CoW from literature10.


Results, Conclusions, and Discussions::
We report flow velocities and WSS of a healthy control and sex-matched, adult patient with SCD. The results of this analysis for the healthy control model showed outflow velocities between 24 and 56 cm/s in the middle cerebral artery (MCA), while the outflow velocities in the anterior cerebral artery (ACA) were between 13 and 35 cm/s and in the PCA ranging from 3 and 8 cm/s. The average WSS in the healthy control model ranged from 0 to 26 dynes/cm2 (Figure 3) .


The SCD model analysis showed elevated outflow velocities in the MCA ranging from 44 and 114 cm/s, in the ACA ranging from 15 and 43 cm/s and in the PCA ranging from 12 and 47 cm/s (Figure 2). Additionally, average WSS in the CoW ranged from 0 to 45 dynes/cm2 with increased WSS at the bifurcations of the MCA and ICA (Figure 3). Pressure in the CoW of the SCD model was higher than the control model at the MCA and ICA with pressures between 80 and 140 mmHg (Figure 4). These results are consistent with the findings reported in the literature showing elevated blood velocities in adults with SCD, yet velocities did not reach 200 cm/s, which is the standard for identifying children with SCD at risk of stroke3. This highlights that patient-specific modeling is imperative to performing more accurate simulations and discovering velocity thresholds that could be used for adults.


The limitations of this preliminary analysis include the sample size (n=2) and the use of simplified boundary conditions to represent the distal cerebrovascular physiology. Our current model does not account for complex cerebral mechanisms such as autoregulation. Future work will apply this model to a larger cohort as well as incorporate more patient-specific boundary conditions, by implementing a more sophisticated lumped parameter network that combines various electric components to represent cerebral microvasculature. In conclusion, the implementation of CFD enables patient-specific cerebrovascular modeling, facilitating the identification of hemodynamic parameters associated with disease progression and may offer valuable insights into potential treatment modalities for patients with SCD.



Acknowledgements (Optional): :
Lara Abdelmohsen1, Anyssa Oden1, Tamer S. Ibrahim2, Enrico M. Novelli3,4, Noelia Grande Gutiérrez 1,5, Sossena Wood1

1Carnegie Mellon University, Department of Biomedical Engineering

2University of Pittsburgh, Department of Bioengineering

3University of Pittsburgh, Heart, Lung and Blood Vascular Medicine Institute

4University of Pittsburgh, Department of Medicine, University of Pittsburgh

5Carnegie Mellon University, Department of Mechanical Engineering

References (Optional): :

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