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Abstract #3785

Towards automated fetal brain biometry reporting for 3D T2w 0.55-3T MRI at 19-40 weeks gestational age range

Aysha Luis1,2, Alena Uus1,3, Jacqueline Matthew1,3, Sophie Arulkumaran1,4, Alexia Egloff Collado1,5, Vanessa Kyriakopoulou1, Sara Neves Silva1, Jordina Aviles Verdera1, Megan Hall1,6, Sarah McElroy1,7, Kathleen Colford1, Joseph V. Hajnal1,3,8, Jana Hutter1,3,9, Lisa Story1,10,11, and Mary Rutherford1
1Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Department of Neuroradiology, Barts Health NHS Trust, London, United Kingdom, 3Biomedical Computing Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 4Department of Neuroradiology, St George's University Hospitals NHS Foundation Trust, London, United Kingdom, 5Department of Neuroradiology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom, 6Department of Women and Children’s Health, King’s College London, London, United Kingdom, 7MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, 8Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 9Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany, 10Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom, 11Department of Women and Children’s Health, King's College London, London, United Kingdom

Synopsis

Keywords: Prenatal, Fetal, biometry, automation, growth charts

Motivation: Previous published work has focused on technical feasibility of automated fetal brain biometry methods. Enabling reliable automated biometry directly during MRI examinations could greatly add to clinical utility.

Goal(s): To automate fetal brain biometry measurements and centile calculations from 3D motion-corrected T2w MRI.

Approach: Automated extraction of fetal brain biometry measurements using deep learning localisation of anatomical landmarks.

Results: Our study automates 11 routinely reported fetal brain measurements trained on a large cohort of control subjects, across a wide range of gestational ages, field strengths and scanning parameters.

Impact: The benefits of automating the time-consuming manual biometry method include improved diagnostic accuracy, confidence and reliability of derived measurements, enabling precise quantification of fetal brain development, as well as improved workflow efficiency and turnaround time for radiology reports.

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Abstract

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