Abstract #3613

Learning how to Breath: Automatic Fetal Breathing Motion Identification and Quantification using MRI

Kelly Payette^1,2,3, Johannes Steinweg^2,3, Jordina Aviles Verdera^2,3, Pablo Lamata³, Erdem Goksen^2,3, Alena U. Uus^2,3, Megan Hall^2,4, Lisa Story^2,3,4, Joseph V. Hajnal^2,3, Mary A. Rutherford², and Jana Hutter^2,3,5

¹Center for MR-Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland, ²Department of Early Life Imaging, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ³Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ⁴Department of Women & Children's Health, King's College London, London, United Kingdom, ⁵Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany

Synopsis

Keywords: Fetal, Fetal

Motivation: A baby's first breath is a critical step of birth. To prepare, the fetus practices with fetal breathing movements (FBM), essential for normal lung development.

Goal(s): Currently, FBMs are measured with manual observation during ultrasound. We demonstrate the usage of magnetic resonance imaging (MRI) to automatically and objectively measure FBMs.

Approach: We acquire 100 dynamic fetal scans throughout gestation using low-field MRI, and demonstrate FBMs by automatically segmenting the fetal lungs in a dynamic MRI scan and investigate changes in volume and mean T2*.

Results: FBMs were detected in 37% of the scans, matching the expected occurrence rates.

Impact: With further testing and validation, the automated detection of fetal breathing movements could become a useful and widespread clinical tool to quantify fetal pulmonary health.

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