Abstract #0166

Placental blood-flow velocity quantification from diffusion MRI

ZhuangJian Yang¹, D.M. Cruz de Oliveira², Leevi Kerkelä¹, Marco Palombo^3,4, Elizabeth Powell¹, Christopher S. Parker¹, Daniel Cromb⁵, Lisa Story⁶, Serena Counsell⁵, Kelly Payette^5,7,8, Joseph V. Hajnal^5,7, Jana Hutter^5,7,9, Rebecca J. Shipley², Daniel C. Alexander¹, and Paddy J. Slator^3,4

¹Hawkes Institute and Department of Computer Science, University College London, London, United Kingdom, ²Department of Mechanical Engineering, University College London, London, United Kingdom, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁴School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom, ⁵Early Life Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁶Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom, ⁷Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁸Center for MR-Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland, ⁹Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany

Synopsis

Keywords: Placenta, Velocity & Flow, Monte Carlo dMRI simulations; Machine Learning

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.

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