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Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling

Research output: Chapter in Book/Report/Conference proceedingConference paper

Standard

Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. / Flouri, Dimitra; Owen, David; Aughwane, Rosalind; Mufti, Nada; Sokolska, Magdalena; Atkinson, David; Kendall, Giles; Bainbridge, Alan; Vercauteren, Tom; David, Anna L.; Ourselin, Sebastien; Melbourne, Andrew.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Vol. 11766 SPRINGER, 2019. p. 609-616 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Flouri, D, Owen, D, Aughwane, R, Mufti, N, Sokolska, M, Atkinson, D, Kendall, G, Bainbridge, A, Vercauteren, T, David, AL, Ourselin, S & Melbourne, A 2019, Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. vol. 11766, Lecture Notes in Computer Science, SPRINGER, pp. 609-616, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/2019. https://doi.org/10.1007/978-3-030-32248-9_68

APA

Flouri, D., Owen, D., Aughwane, R., Mufti, N., Sokolska, M., Atkinson, D., Kendall, G., Bainbridge, A., Vercauteren, T., David, A. L., Ourselin, S., & Melbourne, A. (2019). Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (Vol. 11766, pp. 609-616). (Lecture Notes in Computer Science). SPRINGER. https://doi.org/10.1007/978-3-030-32248-9_68

Vancouver

Flouri D, Owen D, Aughwane R, Mufti N, Sokolska M, Atkinson D et al. Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. In Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Vol. 11766. SPRINGER. 2019. p. 609-616. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-32248-9_68

Author

Flouri, Dimitra ; Owen, David ; Aughwane, Rosalind ; Mufti, Nada ; Sokolska, Magdalena ; Atkinson, David ; Kendall, Giles ; Bainbridge, Alan ; Vercauteren, Tom ; David, Anna L. ; Ourselin, Sebastien ; Melbourne, Andrew. / Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Vol. 11766 SPRINGER, 2019. pp. 609-616 (Lecture Notes in Computer Science).

Bibtex Download

@inbook{34a3cb813f11444dbea8892e20a82861,
title = "Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling",
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 is observed to decrease at This technique provides a robust framework for analysing longitudinal changes in both normal and pathological placental function.",
author = "Dimitra Flouri and David Owen and Rosalind Aughwane and Nada Mufti and Magdalena Sokolska and David Atkinson and Giles Kendall and Alan Bainbridge and Tom Vercauteren and David, {Anna L.} and Sebastien Ourselin and Andrew Melbourne",
year = "2019",
doi = "10.1007/978-3-030-32248-9_68",
language = "English",
isbn = "9783030322472",
volume = "11766",
series = "Lecture Notes in Computer Science",
publisher = "SPRINGER",
pages = "609--616",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
note = "22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Improved Placental Parameter Estimation Using Data-Driven Bayesian Modelling

AU - Flouri, Dimitra

AU - Owen, David

AU - Aughwane, Rosalind

AU - Mufti, Nada

AU - Sokolska, Magdalena

AU - Atkinson, David

AU - Kendall, Giles

AU - Bainbridge, Alan

AU - Vercauteren, Tom

AU - David, Anna L.

AU - Ourselin, Sebastien

AU - Melbourne, Andrew

PY - 2019

Y1 - 2019

N2 - 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 is observed to decrease at This technique provides a robust framework for analysing longitudinal changes in both normal and pathological placental function.

AB - 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 is observed to decrease at This technique provides a robust framework for analysing longitudinal changes in both normal and pathological placental function.

UR - http://www.scopus.com/inward/record.url?scp=85075675697&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32248-9_68

DO - 10.1007/978-3-030-32248-9_68

M3 - Conference paper

AN - SCOPUS:85075675697

SN - 9783030322472

VL - 11766

T3 - Lecture Notes in Computer Science

SP - 609

EP - 616

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - SPRINGER

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -

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