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Visualizing the Placental Energy State in Vivo

  • Shyamalakshmi HaridasanEmail author
  • Bernhard Preim
  • Christian Nasel
  • Gabriel Mistelbauer
Conference paper
  • 14 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

The human placenta is vital for the intrauterine growth and development of fetus. It serves several vital functions, including the transmission of nutrients and hormones from the maternal to the fetal circulatory system. During pregnancy, partial infarcts, thrombosis or hemorrhage within the placenta may affect or even reduce the functional regions maintaining the exchange of hormones, oxygen and nutrients with the fetus. This poses a risk to fetal development and should be monitored, since, at a certain point, the nutritious support might not be sufficient anymore. To assess the functional placental tissue, diffusion tensor magnetic resonance imaging (DT-MRI) is used to discriminate different levels of the placental functional state. Highly active regions contain the so-called cotyledons, units that support the fetus with nutrients. In case of their failure, the fetus gets deprived of sufficient nutritious support, which potentially leads to placental intrauterine growth restriction (IUGR). The direct measurement of the functional state of the cotyledons could provide meaningful insight into the current placental energy state. In this paper, we propose a workflow for extracting and visualizing the functional state of a single cotyledon and a combined visualization depicting the energy state of the entire placenta. We provide informal feedback from a radiologist with experience in placental functional data along 17 data sets.

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Literatur

  1. 1.
    Otake Y, Kanazawa H, Takahashi H, et al. Magnetic resonance imaging of the human placental cotyledon: proposal of a novel cotyledon appearance score. European Journal of Obstetrics and Gynecology and Reproductive Biology. 2019;232:82–86.Google Scholar
  2. 2.
    Javor D, Nasel C, Schweim T, et al. In vivo assessment of putative functional placental tissue volume in placental intrauterine growth restriction (IUGR) in human fetuses using diffusion tensor magnetic resonance imaging. Placenta. 2013;34:676–680.Google Scholar
  3. 3.
    Javor D, Nasel C, Dekan S, et al. Placental MRI shows preservation of brain volume in growth-restricted fetuses who suffer substantial reduction of putative functional placenta tissue (PFPT). Eur J Radiol. 2018;108:189–193.Google Scholar
  4. 4.
    Zun Z, Zaharchuk G, Andescavage N, et al. Non-Invasive placental perfusion imaging in pregnancies complicated by fetal heart disease using Velocity-Selective arterial spin labeled MRI. Sci Rep. 2017;7(16126):1–10.Google Scholar
  5. 5.
    Luo J, Turk E, Bibbo C, et al. In vivo quantification of placental insufficiency by BOLD MRI: a human study. Sci Rep. 2017;7(3713):1–10.Google Scholar
  6. 6.
    Do QN, Lewis MA, Madhuranthakam AJ, et al. Texture analysis of magnetic resonance images of the human placenta throughout gestation: a feasibility study. PLoS One. 2019;14:e0211060.Google Scholar
  7. 7.
    Miao H, Mistelbauer G, Karimov A, et al. Placenta maps: in utero placental health assessment of the human fetus. IEEE Trans Vis Comput Graph. 2017;23:1612–1623.Google Scholar
  8. 8.
    Basser PJ, Pajevic S, Pierpaoli C, et al. In vivo fiber tractography using DT-MRI data. Magn Reson Med. 2000;44:625–6Google Scholar
  9. 9.
    World Medical Association. Declaration of helsinki: ethical principles for medical research involving human subjects; Accessed 2020/01/05. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethicalprinciples-for-medical-research-involving-human-subjects/.32.
  10. 10.
    European Medicines Agency. ICH topic e6 (r2): guideline for good clinical practice; Accessed 2020/01/05. https://www.ema.europa.eu/en/ich-e6-r2-goodclinical-practice.
  11. 11.
    Kang HW. G-wire: a livewire segmentation algorithm based on a generalized graph formulation. Pattern Recognit Lett. 2005;26:2042–2051.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Shyamalakshmi Haridasan
    • 1
    Email author
  • Bernhard Preim
    • 1
  • Christian Nasel
    • 2
    • 3
  • Gabriel Mistelbauer
    • 1
  1. 1.Dept. Simulation and GraphicsOtto-von-Guericke University MagdeburgMagdeburgDeutschland
  2. 2.Dept. of RadiologyUniversity Hospital Tulln, Karl Landsteiner University of Health SciencesKrems an der DonauÖsterreich
  3. 3.Dept. of Radiology and Nuclear MedicineMedical University of ViennaWienÖsterreich

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