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Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling

Flouri, D; David, A; Atkinson, D; Pratt, R; Mufti, N; Sokolska, M; Ourselin, S; ... Melbourne, A; + view all (2019) Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. In: Shen, D and Liu, T and Peters, TM and Staib, LH and Essert, C and Zhou, S and Yap, PT and Khan, A, (eds.) Medical Image Computing and Computer Assisted Intervention: MICCAI 2019 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III. (pp. pp. 609-616). Springer: Cham, Switzerland.

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Abstract

The placenta plays a key contribution to successful pregnancy outcome. New MR imaging techniques are able to reveal intricate details about placental structure and function and measure placental blood flow and feto-placental oxygenation. Placental diffusion-weighted MRI is however challenging due to maternal breathing motion and poor signal-to-noise ratio making motion correction important for subsequent quantitative analysis. In this work, we (i) introduce an iterative model-based registration technique which incorporates a placenta-specific model into the motion correction process and (ii) describe a new technique making use of a Bayesian shrinkage prior to obtain robust estimates of individual and population trends in parameters. Our results suggest that the proposed registration method improves alignment of placental data and that the Bayesian fitting technique allows the estimation of voxel-level placenta flow parameters and the population trend in each parameter with gestational age (GA). We report gestational age dependent differences in vascular compartments and fetal oxygen saturation values observed across 9 normally grown pregnancies between 25–34 weeks gestational age and show qualitatively improved parameter mapping and more precise longitudinal fitting. Fetal oxygen saturation ( FO2 ) is observed to decrease at FO2=−3.6(GAweeks)+190.2(%) . This technique provides a robust framework for analysing longitudinal changes in both normal and pathological placental function.

Type: Proceedings paper
Title: Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Location: Shen Zhen, China
Dates: 13 October 2019 - 17 October 2019
ISBN-13: 978-3-030-32247-2
DOI: 10.1007/978-3-030-32248-9_68
Publisher version: https://doi.org/10.1007/978-3-030-32248-9_68
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Metabolism and Experi Therapeutics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10085533
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