Patient-individualized boundary conditions for CFD simulations using time-resolved 3D angiography

  • Marco Boegel
  • Sonja Gehrisch
  • Thomas Redel
  • Christopher Rohkohl
  • Philip Hoelter
  • Arnd Doerfler
  • Andreas Maier
  • Markus Kowarschik
Original Article

Abstract

Purpose

Hemodynamic simulations are of increasing interest for the assessment of aneurysmal rupture risk and treatment planning. Achievement of accurate simulation results requires the usage of several patient-individual boundary conditions, such as a geometric model of the vasculature but also individualized inflow conditions.

Methods

We propose the automatic estimation of various parameters for boundary conditions for computational fluid dynamics (CFD) based on a single 3D rotational angiography scan, also showing contrast agent inflow. First the data are reconstructed, and a patient-specific vessel model can be generated in the usual way. For this work, we optimize the inflow waveform based on two parameters, the mean velocity and pulsatility. We use statistical analysis of the measurable velocity distribution in the vessel segment to estimate the mean velocity. An iterative optimization scheme based on CFD and virtual angiography is utilized to estimate the inflow pulsatility. Furthermore, we present methods to automatically determine the heart rate and synchronize the inflow waveform to the patient’s heart beat, based on time–intensity curves extracted from the rotational angiogram. This will result in a patient-individualized inflow velocity curve.

Results

The proposed methods were evaluated on two clinical datasets. Based on the vascular geometries, synthetic rotational angiography data was generated to allow a quantitative validation of our approach against ground truth data. We observed an average error of approximately \(5.7\,\%\) for the mean velocity, \(7.1\,\%\) for the pulsatility. The heart rate was estimated very precisely with an average error of about \(0.8\,\%\), which corresponds to about 6 ms error for the duration of one cardiac cycle. Furthermore, a qualitative comparison of measured time–intensity curves from the real data and patient-specific simulated ones shows an excellent match.

Conclusion

The presented methods have the potential to accurately estimate patient-specific boundary conditions from a single dedicated rotational scan.

Keywords

Angiography Computational fluid dynamics Hemodynamics Cone beam CT Flow quantification 

Notes

Acknowledgments

The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG) in the framework of the German excellence initiative.

Compliance with ethical standards

Conflict of interest

S. Gehrisch, T. Redel and M. Kowarschik are employees of Siemens Healthcare GmbH.

Ethical approval

This study has been performed retrospectively. Formal consent is not required.

Informed consent

For this type of study, formal consent is not required.

References

  1. 1.
    Bögel M, Hölter P, Redel T, Maier A, Hornegger J, Dörfler A. (2015) A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th annual international conference of the IEEE, pp 2006–2009Google Scholar
  2. 2.
    Bonnefous O, Pereira V, Ouared R, Brina O, Aerts H, Hermans R, van Nijnatten F, Stawiaski J, Ruijters D (2012) Quantification of arterial flow using digital subtraction angiography. Med Phys 39(10):6264–6275CrossRefPubMedGoogle Scholar
  3. 3.
    Cebral JR, Radaelli A, Frangi A, Putman CM (2007) Qualitative comparison of intra-aneurysmal flow structures determined from conventional and virtual angiograms. In: Medical imaging, pp 65,111E–65,111E. International Society for Optics and PhotonicsGoogle Scholar
  4. 4.
    Davis B, Royalty K, Kowarschik M, Rohkohl C, Oberstar E, Aagaard-Kienitz B, Niemann D, Ozkan O, Strother C, Mistretta C (2013) 4D digital subtraction angiography: implementation and demonstration of feasibility. Am J Neuroradiol 34(10):1914–1921CrossRefPubMedGoogle Scholar
  5. 5.
    Durant J, Waechter I, Hermans R, Weese J, Aach T (2008) Toward quantitative virtual angiography: evaluation with in vitro studies. In: Biomedical imaging: from nano to macro, 2008. ISBI 2008. 5th IEEE international symposium on, pp 632–635Google Scholar
  6. 6.
    Endres J, Kowarschik M, Redel T, Sharma P, Mihalef V, Hornegger J, Dörfler A (2012) A workflow for patient-individualized virtual angiogram generation based on CFD simulation. Comput Math Methods Med 2012(306765):1–24CrossRefGoogle Scholar
  7. 7.
    Endres J, Redel T, Kowarschik M, Hutter J, Hornegger J, Dörfler A (2012) Virtual angiography using CFD simulations based on patient-specific parameter optimization. In: IEEE (ed.) International symposium on biomedical imaging (ISBI), pp 1200–1203Google Scholar
  8. 8.
    Endres J, Rohkohl C, Schafer S, Royalty K, Maier A, Kowarschik M, Hornegger J (2015) 4D DSA iterative reconstruction. In: King M, Glick S, Mueller K (eds) Proceedings of the fully3D, pp 276–279Google Scholar
  9. 9.
    Ford MD, Stuhne GR, Nikolov HN, Habets DF, Lownie SP, Holdsworth DW, Steinman D (2005) Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics. Med Imaging, IEEE Trans 24(12):1586–1592CrossRefGoogle Scholar
  10. 10.
    Gülsün M, Tek H (2008) Robust vessel tree modeling. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer-assisted intervention—MICCAI. Springer, Heidelberg, pp 602–611Google Scholar
  11. 11.
    Huang T, Wu T, Lin C, Mok G, Guo W (2012) Peritherapeutic quantitative flow analysis of arteriovenous malformation on digital subtraction angiography. J Vasc Surg 56(3):812–815CrossRefPubMedGoogle Scholar
  12. 12.
    Karmonik C, Klucnik R, Benndorf G (2008) Blood flow in cerebral aneurysms: comparison of phase contrast magnetic resonance and computational fluid dynamics-preliminary experience. Rofo 180(3):209–215CrossRefPubMedGoogle Scholar
  13. 13.
    Sun Q, Groth A, Bertram M, Waechter I, Bruijns T, Hermans R, Aach T (2010) Phantom-based experimental validation of computational fluid dynamics simulations on cerebral aneurysms. Med Phys 37(9):5054–5065Google Scholar
  14. 14.
    Sun Q, Groth A, Waechter I, Brina O, Weese J, Aach T (2009) Quantitative evaluation of virtual angiography for interventional X-ray acquisitions. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro: [ISBI ’09], IEEE, 2009, Boston, pp 895–898Google Scholar
  15. 15.
    Wächter I, Bredno J, Hermans R, Weese J, Barrat D, Hawkes D (2008) Model-based blood flow quantification from rotational angiography. Med Image Anal 12(5):586–602CrossRefGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander Universität Erlangen-NürnbergErlangenGermany
  2. 2.Siemens Healthcare GmbHForchheimGermany
  3. 3.Department of NeuroradiologyUniversitätsklinikum ErlangenErlangenGermany

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