In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study
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Author(s)
Luo, Jie
Abaci Turk, Esra
Bibbo, Carolina
Gagoski, Borjan
Roberts, Drucilla J.
Vangel, Mark
Tempany-Afdhal, Clare M.
Barnewolt, Carol
Estroff, Judy
Palanisamy, Arvind
Date Issued
June 2017
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Luo, Jie, Esra Abaci Turk, Carolina Bibbo, Borjan Gagoski, Drucilla J. Roberts, Mark Vangel, Clare M. Tempany-Afdhal, et al. “In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study.” Scientific Reports 7, 1 (June 2017): 3713 © 2017 The Author(s)
Version
Final published version
Abstract
Fetal health is critically dependent on placental function, especially placental transport of oxygen from mother to fetus. When fetal growth is compromised, placental insufficiency must be distinguished from modest genetic growth potential. If placental insufficiency is present, the physician must trade off the risk of prolonged fetal exposure to placental insufficiency against the risks of preterm delivery. Current ultrasound methods to evaluate the placenta are indirect and insensitive. We propose to use Blood-Oxygenation-Level-Dependent (BOLD) MRI with maternal hyperoxia to quantitatively assess mismatch in placental function in seven monozygotic twin pairs naturally matched for genetic growth potential. In-utero BOLD MRI time series were acquired at 29 to 34 weeks gestational age. Maps of oxygen Time-To-Plateau (TTP) were obtained in the placentas by voxel-wise fitting of the time series. Fetal brain and liver volumes were measured based on structural MR images. After delivery, birth weights were obtained and placental pathological evaluations were performed. Mean placental TTP negatively correlated with fetal liver and brain volumes at the time of MRI as well as with birth weights. Mean placental TTP positively correlated with placental pathology. This study demonstrates the potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution 4.0 International (CC BY 4.0)
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DOI of Published Version
https://doi.org/10.1038/S41598-017-03450-0
