Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI

Paddy J. Slator*, Daniel Cromb, Laurence H. Jackson, Alison Ho, Serena J. Counsell, Lisa Story, Lucy C. Chappell, Mary Rutherford, Joseph V. Hajnal, Jana Hutter, Daniel C. Alexander

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: In-vivo measurements of placental structure and function have the potential to improve prediction, diagnosis, and treatment planning for a wide range of pregnancy complications, such as fetal growth restriction and pre-eclampsia, and hence inform clinical decision making, ultimately improving patient outcomes. MRI is emerging as a technique with increased sensitivity to placental structure and function compared to the current clinical standard, ultrasound. Methods: We demonstrate and evaluate a combined diffusion-relaxation MRI acquisition and analysis pipeline on a sizable cohort of 78 normal pregnancies with gestational ages ranging from 15 + 5 to 38 + 4 weeks. Our acquisition comprises a combined T2*-diffusion MRI acquisition sequence - which is simultaneously sensitive to oxygenation, microstructure and microcirculation. We analyse our scans with a data-driven unsupervised machine learning technique, InSpect, that parsimoniously identifies distinct components in the data. Results: We identify and map seven potential placental microenvironments and reveal detailed insights into multiple microstructural and microcirculatory features of the placenta, and assess their trends across gestation. Discussion: By demonstrating direct observation of micro-scale placental structure and function, and revealing clear trends across pregnancy, our work contributes towards the development of robust imaging biomarkers for pregnancy complications and the ultimate goal of a normative model of placental development.

Original languageEnglish
Pages (from-to)29-37
Number of pages9
JournalPlacenta
Volume144
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Diffusion MRI
  • Diffusion-relaxation MRI
  • Placenta
  • Unsupervised learning

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