Comparing Subjects with Reference Populations - A Visualization Toolkit for the Analysis of Aortic Anatomy and Pressure Distribution

  • Sahar KarimkeshtehEmail author
  • Lilli Kaufhold
  • Sarah Nordmeyer
  • Lina Jarmatz
  • Andreas Harloff
  • Anja Hennemuth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11504)


The analysis of anatomical and hemodynamic vessel parameters plays an important role in diagnosis and therapy planning for aortic diseases. Normal values and decision thresholds are usually based on global or local parameters provided by population studies. In order to enable a more holistic comparison of a single subject and a matching reference population we have developed a spatiotemporal normalization concept for the analysis of 4D PC MRI data of the thoracic aorta. This enables the comparison of geometric properties and pressure differences along the vessel course as well as in a sector model, which represents a cross-sectional value distribution. We tested the applicability of the presented approach by comparing subjects with aortic diseases to matching subgroups of a normal reference population. The presented framework enabled a visual and quantitative assessment of the local geometric and pressure distribution changes of different pathological alterations of the aorta. It will be extended to integrate further hemodynamic properties and larger reference cohorts to support clinical decision making based on hemodynamic information in near future.


4D PC MRI Aortic disease Pressure distribution Population study 


  1. 1.
    ESC Committee for Practice Guidelines: 2014 ESC Guidelines on the diagnosis and treatment of aortic diseases. Eur. Heart J. 35(41), 2873–2926 (2014). Epub 29 Aug 2014
  2. 2.
    Rylski, B., Desjardins, B., Moser, W., Bavaria, J.E., Milewski, R.K.: Gender-related changes in aortic geometry throughout life. Eur. J. Cardiothorac. Surg. 45(5), 805–811 (2014). Scholar
  3. 3.
    Redheuil, A., et al.: Age-related changes in aortic arch geometry: relationship with proximal aortic function and left ventricular mass and remodeling. J. Am. Coll. Cardiol. 58(12), 1262–1270 (2011)CrossRefGoogle Scholar
  4. 4.
    Garcia, J., et al.: Distribution of blood flow velocity in the normal aorta: effect of age and gender. J. Magn. Reson. Imaging 47(2), 487–498 (2018)CrossRefGoogle Scholar
  5. 5.
    Harloff, A., et al.: Determination of aortic stiffness using 4D flow cardiovascular magnetic resonance - a population-based study. J. Cardiovasc. Magn. Reson. 20(1), 43 (2018)CrossRefGoogle Scholar
  6. 6.
    Mistelbauer, G., Schmidt, J., Sailer, A., Bäumler, K., Walters, S., Fleischmann, D.: Aortic dissection maps: comprehensive visualization of aortic dissections for risk assessment. In: VCBM (2016)Google Scholar
  7. 7.
    Behrendt, B., Ebel, S., Gutberlet, M., Preim, B.: A framework for visual comparison of 4D PC-MRI aortic blood flow data. In: VCBM (2018)Google Scholar
  8. 8.
    Lamata, P., et al.: Aortic relative pressure components derived from four-dimensional flow cardiovascular magnetic resonance. Magn. Reson. Med. 72, 1162–1169 (2014)CrossRefGoogle Scholar
  9. 9.
    Bock, J., et al.: In vivo non-invasive 4D pressure difference mapping in the human aorta: phantom comparison and application in healthy volunteers and patients. Magn. Reson. Med. 66(4), 1079–1088 (2011)CrossRefGoogle Scholar
  10. 10.
    Ebbers, T., Wigstrom, L., Bolger, A.F., Engvall, J., Karlsson, M.: Estimation of relative cardiovascular pressures using time-resolved three-dimensional phase contrast MRI. Magn. Reson. Med. 45(5), 872–879 (2001)CrossRefGoogle Scholar
  11. 11.
    Tyszka, J.M., Laidlaw, D.H., Asa, J.W., Silverman, J.M.: Three-dimensional, time-resolved (4D) relative pressure mapping using magnetic resonance imaging. J. Magn. Reson. Imaging 12(2), 321–329 (2000)CrossRefGoogle Scholar
  12. 12.
    Hennemuth, A., et al.: Fast interactive exploration of 4D MRI flow data. In: Wong, K.H., et al. (eds.) SPIE Medical Imaging, vol. 7964, 79640E, pp. 1–11. SPIE (2011)Google Scholar
  13. 13.
    Meier, S., Hennemuth, A., Drexl, J., Bock, J., Jung, B., Preusser, T.: A fast and noise-robust method for computation of intravascular pressure difference maps from 4D PC-MRI data. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 215–224. Springer, Heidelberg (2013). Scholar
  14. 14.
    Riesenkampff, E., et al.: Pressure fields by flow-sensitive, 4D, velocity-encoded CMR in patients with aortic coarctation. JACC Cardiovasc. Imaging 7(9), 920–926 (2014)CrossRefGoogle Scholar
  15. 15.
    Mirzaee, H., et al.: MRI-based computational hemodynamics in patients with aortic coarctation using the lattice Boltzmann methods: clinical validation study. J. Magn. Reson. Imaging 45, 139–146 (2017). Scholar
  16. 16.
    Stalder, A., Russe, M., Frydrychowicz, A., Bock, J., Hennig, J., Markl, M.: Quantitative 2D and 3D phase contrast MRI: optimized analysis of blood flow and vessel wall parameters. Magn. Reson. Med. 60, 1218–1231 (2008)CrossRefGoogle Scholar
  17. 17.
    Liu, J., Shar, J.A., Sucosky, P.: Wall shear stress directional abnormalities in BAV aortas: toward a new hemodynamic predictor of aortopathy? Front Physiol. 14(9), 993 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sahar Karimkeshteh
    • 1
    • 2
    Email author
  • Lilli Kaufhold
    • 1
    • 2
  • Sarah Nordmeyer
    • 3
  • Lina Jarmatz
    • 2
  • Andreas Harloff
    • 4
  • Anja Hennemuth
    • 1
    • 2
  1. 1.Fraunhofer MEVISBremenGermany
  2. 2.Charité – Universitätsmedizin BerlinBerlinGermany
  3. 3.Deutsches Herzzentrum BerlinBerlinGermany
  4. 4.Universitätsklinikum FreiburgFreiburgGermany

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