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Automated Craniofacial Biometry with 3D T2w Fetal MRI
Jacqueline Matthew1,2, Alena Uus1, Alexia Egloff Collado1,2
1Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Medrxiv : the Preprint Server for Health Sciences
|August 26, 2024
Summary
This study introduces an automated fetal MRI method for precise craniofacial measurements, identifying key differences in Down syndrome (T21) fetuses. The pipeline significantly reduces measurement time and improves accuracy for prenatal diagnosis.
Area of Science:
- Medical Imaging
- Fetal Medicine
- Genetics
Background:
- Prenatal craniofacial evaluation is crucial for diagnosing genetic conditions but is subjective and challenging with 3D ultrasound.
- Automated methods are needed to improve accuracy and efficiency in fetal craniofacial measurements.
Purpose of the Study:
- To develop and validate an automated pipeline for fetal craniofacial biometrics using 3D motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI.
- To identify significant craniofacial differences in fetuses with Down syndrome (T21) compared to controls.
Main Methods:
- An MRI atlas with anatomical landmarks was used for automated registration, auto-labelling, and biometric calculation.
- 108 healthy controls and 24 fetuses with T21 (29-36 weeks GA) were analyzed.
- Reliability and reproducibility were assessed by four observers.
Main Results:
- The automated pipeline achieved a 0.03% landmark placement error rate.
- Seven measurements, including anterior base of skull and maxillary length, showed significant differences between T21 and control groups (p<0.001).
- Automated measurements took ~5 minutes, compared to 25-35 minutes for manual measurements.
Conclusions:
- This is the first automated atlas-based protocol for fetal craniofacial biometrics using 3D SVR MRI.
- The method accurately reveals morphological differences in T21 fetuses, aiding prenatal diagnosis.
- Future work will focus on enhancing reliability and expanding to larger clinical cohorts.
