Year: 2024
Type: article
Abstract: OBJECTIVES: Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. METHODS: A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. RESULTS: Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research. CONCLUSION: This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.
Source: PLOS Digital Health
Authors: Jacqueline Matthew, Alena Uus, Alexia Egloff Collado, Aysha Luis, Sophie Arulkumaran, Abi Fukami-Gartner, Vanessa Kyriakopoulou, Daniel Cromb, Robert Wright, Kathleen Colford, Maria Deprez, Jana Hutter, Jonathan O’Muircheartaigh, Christina Malamateniou, Reza Razavi, Lisa Story, Jo Hajnal, Mary A. Rutherford
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,926
Funders: Wellcome Trust, National Institute for Health and Care Research, King's College London, European Commission, Medical Research Council Centre for Neurodevelopmental Disorders, National Institutes of Health, NIHR Maudsley Biomedical Research Centre, Medical Research Council, Engineering and Physical Sciences Research Council, Centre For Medical Engineering, King’s College London, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1054121244, G1114913786, G1211483440, G1247657515, G1307489074, King's College London Medical Engineering Centre of Research Excellence, G1814604835, G2084819120, G2119833926, A computer-guided imaging system for prenatal screening and comprehensive diagnosis of fetal abnormalities., G2417159676, G3101159349, G3164596160, G339059223, MRC Centre for Neurodevelopmental Disorders, G3711895790, New Synthetic Approaches to Prepare Functionalized GAG Oligosaccharides as Basic Tools in Glycomics, Magnetic resonance Imaging of mother and fetus in late gestation to inform and optimise BIRTH management: the MIBIRTH study, Evaluation of state of the art ultrasound and novel three dimensional MRI for fetal facial and cranial examination of dysmorphic features; a pilot study, Imaging Focal Epilepsy in Children: A Developmental Perspective, G5296857752, G5537678747, G5893523844, King's College London Medical Engineering Centre of Research Excellence, G5935007269, Structure and function of the placenta from implantation to delivery: a next generation MRI approach, G6733629873, G7082644654, Imaging Focal Epilepsy in Children: A Developmental Perspective, G8048453011, G8303168876, G8775246936, G938335843, G961285871
OpenAlex ID: W4405894972