Placental blood-flow velocity quantification from diffusion MRI

Motivation: Estimating blood flow velocity in capillary beds lacking voxel-scale coherence is challenging with current imaging techniques. Altered blood flow in small-scale placental capillaries is a key factor affecting the health of pregnant women and their fetuses. Goal(s): Estimating fetoplacental blood velocity directly from diffusion MRI. Approach: We perform Monte Carlo dMRI simulations with perfusion in synthetic capillary systems, training a machine learning algorithm to recover parameters like flow velocity from generated signals. This trained algorithm then estimates these parameters from in-vivo diffusion MRI of human placentas. Results: Our approach estimates placental blood velocities and diffusivities, yielding values comparable with published data. Impact: Our approach estimates blood velocity from existing diffusion MRI data, reflecting placental blood flow conditions and potentially helping to diagnose diseases like pre-eclampsia. This approach could also be applied to organs where blood velocity is clinically significant.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0166
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Placental blood-flow velocity quantification from diffusion MRI

Rebecca J. Shipley, Serena J. Counsell, Daniel C. Alexander, Leevi Kerkelä et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced Neuroimaging Techniques and Applications
article

Placental blood-flow velocity quantification from diffusion MRI

Rebecca J. Shipley, Serena J. Counsell, Daniel C. Alexander, Leevi Kerkelä, Daniel Cromb, Elizabeth Powell, Paddy J. Slator, Jana Hutter, Marco Palombo, Kelly Payette, Dinis Reis Oliveira, Joseph V. Hajnal, Lisa Story, Christopher S. Parker, Z. Yang
article en

Abstract

Motivation: Estimating blood flow velocity in capillary beds lacking voxel-scale coherence is challenging with current imaging techniques. Altered blood flow in small-scale placental capillaries is a key factor affecting the health of pregnant women and their fetuses. Goal(s): Estimating fetoplacental blood velocity directly from diffusion MRI. Approach: We perform Monte Carlo dMRI simulations with perfusion in synthetic capillary systems, training a machine learning algorithm to recover parameters like flow velocity from generated signals. This trained algorithm then estimates these parameters from in-vivo diffusion MRI of human placentas. Results: Our approach estimates placental blood velocities and diffusivities, yielding values comparable with published data. Impact: Our approach estimates blood velocity from existing diffusion MRI data, reflecting placental blood flow conditions and potentially helping to diagnose diseases like pre-eclampsia. This approach could also be applied to organs where blood velocity is clinically significant.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Advanced Neuroimaging Techniques and Applications
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