Rapid Blood Flow Computation on Digital Subtraction Angiography: Preliminary Results

Conference paper


In this study, we simulate blood flow in complex geometries obtained by digital subtraction angiography (DSA) images. We represent the flow domain by a set of irregularly distributed nodes or uniform Cartesian embedded grid, and we numerically solve the non-stationary Navier–Stokes (N-S) equations, in their velocity–vorticity formulation, by using a meshless point collocation method. The spatial derivatives are computed with the discretization corrected particle strength exchange (DC PSE) method, a recently developed meshless interpolation method. For the transient term a fourth order Runge–Kutta time integration scheme is used.


Blood flow Computational fluid dynamics Meshless Navier–Stokes Explicit Runge–Kutta 



This research was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP160100714).


  1. 1.
    Zhang JY, Joldes GR, Wittek A, Miller K (2013) Patient‐specific computational biomechanics of the brain without segmentation and meshing. Int J Numer Methods Biomed Eng 29(2):293–308MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bourantas GC, Cheesman BL, Ramaswamy R, Sbalzarini IF (2016) Using DC PSE operator discretization in Eulerian meshless collocation methods improves their robustness in complex geometries. Comput Fluids 136:285–300MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fletcher CAJ (1988) Computational techniques for fluid dynamics, vol I and II. Springer series in computational physics. Springer, BerlinCrossRefGoogle Scholar
  4. 4.
    Schrader B, Reboux S, Sbalzarini IF (2010) Discretization correction of general integral PSE operators for particle methods. J Comput Phys 229:4159–4182MathSciNetCrossRefGoogle Scholar
  5. 5.
    Degond P, Mas-Gallic S (1989) The weighted particle method for convection-diffusion equations. Part 2: the anisotropic case. Math Comput 53(188):509–525zbMATHGoogle Scholar
  6. 6.
    Eldredge JD, Leonard A, Colonius T (2002) A general deterministic treatment of derivatives in particle methods. J Comput Phys 180(2):686–709CrossRefGoogle Scholar
  7. 7.
    Xiong G, Figueroa CA, Xiao N, Taylor CA (2011) Simulation of blood flow in deformable vessels using subject-specific geometry and spatially varying wall properties. Int J Numer Methods Biomed Eng 27(7):1000–1016MathSciNetCrossRefGoogle Scholar
  8. 8.
    Milner JS, Moore JA, Rutt BK, Steinman DA (1998) Hemodynamics of human carotid artery bifurcations: computational studies with models reconstructed from magnetic resonance imaging of normal subjects. J Vasc Surg 28(1):143–156CrossRefGoogle Scholar
  9. 9.
    Campbell IC, Ries J, Dhawan SS, Quyyumi AA, Taylor WR, Oshinski JN (2012) Effect of inlet velocity profiles on patient-specific computational fluid dynamics simulations of the carotid bifurcation. ASME J Biomech Eng 134(5):051001CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Intelligent Systems for Medicine LaboratoryThe University of Western AustraliaPerthAustralia
  2. 2.The Department of Interventional RadiologyPatras University Hospital, School of MedicineRionGreece
  3. 3.The Department of Interventional RadiologyGuy’s and St. Thomas’ Hospitals, NHS Foundation Trust, King’s Health PartnersLondonUK
  4. 4.Department of Medical PhysicsSchool of Medicine, University of PatrasRionGreece
  5. 5.Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  6. 6.Intelligent Systems for Medicine Laboratory, Department of Mechanical EngineeringThe University of Western AustraliaPerthAustralia

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