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Abstract #0906

A Bayesian Approach for Diffusion-Weighted Imaging to study placenta development and function in pregnancy in a large animal model

Dimitra Flouri1,2, Jack RT Darby3, Stacey L Holman3, Sunthara R Perumal4, Anna L David5,6, Janna L Morrison3, and Andrew Melbourne1,2
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 2Department of Medical Physics & Biomedical Engineering, University College London, London, United Kingdom, 3Early Origins of Adult Health Research Group, University of South Australia, Adelaide, Australia, 4Preclinical Imaging and Research Laboratories, South Australian Health and Medical Research Institute, Adelaide, Australia, 5Institute for Women's Health, University College London, London, United Kingdom, 6NIHR University College London Hospitals Biomedical Research Center, London, United Kingdom

Abnormalities of placental development and function result in fetal growth restriction. There is growing interest in understanding placenta structure and function throughout pregnancy to gain better understanding of placenta dysfunction. Advances in technology enables derivation of quantitative indices that reflect tissue microcapillary perfusion and tissue diffusivity from MRI. Despite recent progress, in-vivo diffusion-weighted MRI remains challenging due to long scan times, respiratory motion and low signal-to-noise ratio. Sheep provide a relevant large-scale model for invasive validation studies for MRI measurements. We aimed to improve parameter mapping using Bayesian inference. Bayesian analysis yields improved parameter maps relative to conventional least-squares fitting.

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