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

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3785
Primary Topic
Fetal and Pediatric Neurological Disorders
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Jacqueline Matthew, Megan Hall, Sarah McElroy, Aysha Luis et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Fetal and Pediatric Neurological Disorders
article

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

Jacqueline Matthew, Megan Hall, Sarah McElroy, Aysha Luis, Alena Uus, Vanessa Kyriakopoulou, Jana Hutter, Alexia Egloff Collado, Jordina Aviles Verdera, Sophie Arulkumaran, Mary Rutherford, Joseph V. Hajnal, Kathleen Colford, Lisa Story, Sara Neves Silva
article en

Abstract

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Fetal and Pediatric Neurological Disorders
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