Abstract
Moyamoya disease (MMD) is characterized by narrowing of the distal internal carotid artery and the circle of Willis (CoW) and leads to recurring ischemic and hemorrhagic stroke. A retrospective review of data from 50 pediatric MMD patients revealed that among the 24 who had a unilateral stroke and were surgically treated, 11 (45.8%) had a subsequent, contralateral stroke. There is no reliable way to predict these events. After a pilot study in Acta−/− mice that have features of MMD, we hypothesized that local hemodynamics are predictive of contralateral strokes and sought to develop a patient-specific analysis framework to noninvasively assess this stroke risk. A pediatric MMD patient with an occlusion in the right middle cerebral artery and a right-sided stroke, who was surgically treated and then had a contralateral stroke, was selected for analysis. By using an unsteady Navier–Stokes solver within an isogeometric analysis framework, blood flow was simulated in the CoW model reconstructed from the patient’s postoperative imaging data, and the results were compared with those from an age- and sex-matched control subject. A wall shear rate (WSR) > 60,000 s−1 (about 12 × higher than the coagulation threshold of 5000 s−1 and 9 × higher than control) was measured in the terminal left supraclinoid artery; its location coincided with that of the subsequent postsurgical left-sided stroke. A parametric study of disease progression revealed a strong correlation between the degree of vascular morphology altered by MMD and local hemodynamic environment. The results suggest that an occlusion in the CoW could lead to excessive contralateral WSRs, resulting in thromboembolic ischemic events, and that WSR could be a predictor of future stroke.
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References
Bazilevs Y, Calo VM, Cottrell JA, Hughes TJR, Reali A, Scovazzi G (2007) Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows. Comput Methods Appl Mech Eng 197:173–201. https://doi.org/10.1016/j.cma.2007.07.016
Casa LD, Deaton DH, Ku DN (2015) Role of high shear rate in thrombosis. J Vasc Surg 61:1068–1080. https://doi.org/10.1016/j.jvs.2014.12.050
Dauser RC, Tuite GF, McCluggage CW (1997) Dural inversion procedure for Moyamoya disease. Technical Note J Neurosurg 86:719–723. https://doi.org/10.3171/jns.1997.86.4.0719
Derdeyn CP (2001) Hemodynamic impairment and stroke risk: Prove it. AJNR Am J Neuroradiol 22:233–234
Fedorov A et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001
Fujimura M, Shimizu H, Inoue T, Mugikura S, Saito A, Tominaga T (2011) Significance of focal cerebral hyperperfusion as a cause of transient neurologic deterioration after extracranial-intracranial bypass for moyamoya disease: comparative study with non-moyamoya patients using N-isopropyl-p-[(123)I]iodoamphetamine single-photon emission computed tomography. Neurosurgery 68:957–964. https://doi.org/10.1227/NEU.0b013e318208f1da (discussion 964-955)
Guey S, Tournier-Lasserve E, Herve D, Kossorotoff M (2015) Moyamoya disease and syndromes: from genetics to clinical management. Appl Clin Genet 8:49–68. https://doi.org/10.2147/TACG.S42772
Hossain SS, Hossainy SFA, Bazilevs Y, Calo VM, Hughes TJR (2012) Mathematical modeling of coupled drug and drug-encapsulated nanoparticle transport in patient-specific coronary artery walls. Comput Mech 49:213–242. https://doi.org/10.1007/s00466-011-0633-2
Hossain SS, Zhang Y, Liang X, Hussain F, Ferrari M, Hughes TJ, Decuzzi P (2013) Silico vascular modeling for personalized nanoparticle delivery. Nanomedicine (Lond) 8:343–357. https://doi.org/10.2217/nnm.12.124
Hossain SS, Hughes TJ, Decuzzi P (2014) Vascular deposition patterns for nanoparticles in an inflamed patient-specific arterial tree. Biomech Model Mechanobiol 13:585–597. https://doi.org/10.1007/s10237-013-0520-1
Hossain SS et al (2015) Magnetic resonance imaging-based computational modelling of blood flow and nanomedicine deposition in patients with peripheral arterial disease. J R Soc Interface. https://doi.org/10.1098/rsif.2015.0001
Hsu M-C, Akkerman I, Bazilevs Y (2011) High-performance computing of wind turbine aerodynamics using isogeometric analysis. Comput Fluids 49:93–100. https://doi.org/10.1016/j.compfluid.2011.05.002
Hughes TJR, Cottrell JA, Bazilevs Y (2005) Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement. Comput Methods Appl Mech Eng 194:4135–4195. https://doi.org/10.1016/j.cma.2004.10.008
Hung SC et al (2014) New grading of moyamoya disease using color-coded parametric quantitative digital subtraction angiography. J Chin Med Assoc 77:437–442. https://doi.org/10.1016/j.jcma.2014.05.007
Jamil M et al (2016) Changes to the geometry and fluid mechanics of the carotid siphon in the pediatric Moyamoya disease. Comput Methods Biomech Biomed Engin 19:1760–1771. https://doi.org/10.1080/10255842.2016.1184655
Kamada F et al (2011) A genome-wide association study identifies RNF213 as the first Moyamoya disease gene. J Hum Genet 56:34–40. https://doi.org/10.1038/jhg.2010.132
Lee M, Zaharchuk G, Guzman R, Achrol A, Bell-Stephens T, Steinberg GK (2009) Quantitative hemodynamic studies in Moyamoya disease: a review. Neurosurg Focus 26:E5. https://doi.org/10.3171/2009.1.FOCUS08300
Lee WJ, Jeong SK, Han KS, Lee SH, Ryu YJ, Sohn CH, Jung KH (2020) Impact of endothelial shear stress on the bilateral progression of unilateral Moyamoya disease. Stroke 51:775–783. https://doi.org/10.1161/STROKEAHA.119.028117
Leng X et al (2014) Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence. PLoS ONE 9:e97531. https://doi.org/10.1371/journal.pone.0097531
McElroy M, Keshmiri A (2018) Impact of using conventional inlet/outlet boundary conditions on haemodynamic metrics in a subject-specific rabbit aorta. Proc Inst Mech Eng Part H J Eng Med 232:103–113
Milewicz DM et al (2010) De novo ACTA2 mutation causes a novel syndrome of multisystemic smooth muscle dysfunction. Am J Med Genet A 152A:2437–2443. https://doi.org/10.1002/ajmg.a.33657
Nagiub M, Allarakhia I (2013) Pediatric Moyamoya disease. Am J Case Rep 14:134–138. https://doi.org/10.12659/AJCR.889170
Parzy E, Miraux S, Franconi JM, Thiaudiere E (2009) In vivo quantification of blood velocity in mouse carotid and pulmonary arteries by ECG-triggered 3D time-resolved magnetic resonance angiography. NMR Biomed 22:532–537. https://doi.org/10.1002/nbm.1365
Rashad S, Saqr KM, Fujimura M, Niizuma K, Tominaga T (2020) The hemodynamic complexities underlying transient ischemic attacks in early-stage Moyamoya disease: an exploratory CFD study. Sci Rep 10:3700. https://doi.org/10.1038/s41598-020-60683-2
Rivera CP, Veneziani A, Ware RE, Platt MO (2016) Original research: sickle cell anemia and pediatric strokes: computational fluid dynamics analysis in the middle cerebral artery. Exp Biol Med (Maywood) 241:755–765. https://doi.org/10.1177/1535370216636722
Schöning M, Hartig B (1998) The development of hemodynamics in the extracranial carotid and vertebral arteries. Ultrasound Med Biol 24:655–662
Scott RM, Smith ER (2009) Moyamoya disease and Moyamoya syndrome. N Engl J Med 360:1226–1237. https://doi.org/10.1056/NEJMra0804622
Seol HJ, Shin DC, Kim YS, Shim EB, Kim SK, Cho BK, Wang KC (2010) Computational analysis of hemodynamics using a two-dimensional model in moyamoya disease. J Neurosurg Pediatr 5:297–301. https://doi.org/10.3171/2009.10.PEDS09452
Shimano K, Serigano S, Ikeda N, Yuchi T, Shiratori S, Nagano H (2019) Understanding of boundary conditions imposed at multiple outlets in computational haemodynamic analysis of cerebral aneurysm. J Biorheol 33:32–42
Smith ER, Scott RM (2008) Progression of disease in unilateral Moyamoya syndrome. Neurosurg Focus 24:E17. https://doi.org/10.3171/FOC/2008/24/2/E17
Starosolski Z, Villamizar CA, Rendon D, Paldino MJ, Milewicz DM, Ghaghada KB, Annapragada AV (2015) Ultra high-resolution in vivo computed tomography imaging of mouse cerebrovasculature using a long circulating blood pool contrast agent. Sci Rep 5:10178. https://doi.org/10.1038/srep10178
Tortora D et al (2018) Noninvasive assessment of hemodynamic stress distribution after indirect revascularization for pediatric Moyamoya vasculopathy. AJNR Am J Neuroradiol 39:1157–1163. https://doi.org/10.3174/ajnr.A5627
Urick BY, Sanders TJ, Hossain S, Zhang Y, Hughes TJ (2019) Review of patient-specific vascular modeling: template-based isogeometric framework and the case for CAD. Arch Comput Meth Eng 26:381–404
Veeravagu A, Guzman R, Patil CG, Hou LC, Lee M, Steinberg GK (2008) Moyamoya disease in pediatric patients: outcomes of neurosurgical interventions. Neurosurg Focus 24:E16. https://doi.org/10.3171/FOC/2008/24/2/E16
Wahlin A, Ambarki K, Birgander R, Wieben O, Johnson KM, Malm J, Eklund A (2013) Measuring pulsatile flow in cerebral arteries using 4D phase-contrast MR imaging. AJNR Am J Neuroradiol 34:1740–1745. https://doi.org/10.3174/ajnr.A3442
Wallace S et al (2016) Disrupted nitric oxide signaling due to GUCY1A3 mutations increases risk for moyamoya disease, achalasia and hypertension. Clin Genet 90:351–360. https://doi.org/10.1111/cge.12739
Yeon JY, Shin HJ, Kong DS, Seol HJ, Kim JS, Hong SC, Park K (2011) The prediction of contralateral progression in children and adolescents with unilateral moyamoya disease. Stroke 42:2973–2976. https://doi.org/10.1161/STROKEAHA.111.622522
Zhang C, Xie S, Li S, Pu F, Deng X, Fan Y, Li D (2012) Flow patterns and wall shear stress distribution in human internal carotid arteries: the geometric effect on the risk for stenoses. J Biomech 45:83–89. https://doi.org/10.1016/j.jbiomech.2011.10.001
Zhang Q et al (2016) Clinical features and long-term outcomes of unilateral moyamoya disease. World Neurosurg 96:474–482. https://doi.org/10.1016/j.wneu.2016.09.018
Zhu F et al (2015) Assessing surgical treatment outcome following superficial temporal artery to middle cerebral artery bypass based on computational haemodynamic analysis. J Biomech 48:4053–4058. https://doi.org/10.1016/j.jbiomech.2015.10.005
Zipfel GJ, Sagar J, Miller JP, Videen TO, Grubb RL Jr, Dacey RG Jr, Derdeyn CP (2009) Cerebral hemodynamics as a predictor of stroke in adult patients with moyamoya disease: a prospective observational study. Neurosurg Focus 26:E6. https://doi.org/10.3171/2009.01.FOCUS08305
Acknowledgements
The authors gratefully acknowledge the assistance of Stephanie Wallace in providing patient imaging data, and the Texas Advanced Computing Center (TACC) at the University of Texas at Austin for providing high-performance computing (HPC) resources that have contributed to the research results reported in this paper. Stephen N. Palmer, PhD, ELS, of the Department of Scientific Publications at the Texas Heart Institute, contributed to the editing of the manuscript. James Philpot of the Department of Visual Communications at the Texas Heart Institute created the artwork in Fig. 1.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Health [grant number R03NS110442]. AA acknowledges funding from the National Institutes of Health (U01DE028233, R01HD094347). DMM has funding for MMD studies from the American Heart Association Merit Award.
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SSH planned and supervised the project, wrote the manuscript, ran the simulations, and interpreted data. ZS and SSH conducted the retrospective review of patient data; ZS performed image analysis; ZS and TS performed image segmentation; TS and MJJ reconstructed the NURBS-based computational models from the segmented imaging data; MCH and MW conducted the numerical implementation of the flow model in a parallelized numerical code; TS, MJJ, and SSH performed post-processing of simulation results; DM advised on the clinical relevance of the research and edited the manuscript; and AA helped with the conceptualization of the project and data interpretation. All authors provided critical feedback, commented on the manuscript, and approved the final submission.
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AA is a consultant to, and a founder and stockholder in, Alzeca Biosciences and a shareholder in Sensulin LLC. ZS is a stockholder in Alzeca Biosciences and a consultant for InContext.ai. All other authors declare that they have no conflict of interest.
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There was no direct involvement of human subjects or protected health information (PHI). All patient imaging data used in analysis and modeling were collected retrospectively from medical records and de-identified at Texas Children’s Hospital. Institutional review board (IRB) approval was obtained with a waiver of written authorization for consent. No animals were involved in this study. Mouse cerebrovascular images used in this work were taken from a previous study conducted by (Starosolski et al. 2015).
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Hossain, S.S., Starosolski, Z., Sanders, T. et al. Image-based patient-specific flow simulations are consistent with stroke in pediatric cerebrovascular disease. Biomech Model Mechanobiol 20, 2071–2084 (2021). https://doi.org/10.1007/s10237-021-01495-9
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DOI: https://doi.org/10.1007/s10237-021-01495-9