560 Fetal craniofacial biometry: feasibility of deep 3D MRI phenotyping in a cohort with Down syndrome using atlas-based label propagation

• Published in: Archives of disease in childhood Vol. 108; no. Suppl 2; pp. A153 - A154
• Main Authors: Matthew, Jacqueline, Uus, Alena, Gartner, Abi, Lucillio Cordero Grande, Kyriakopoulou, Vanessa, Cromb, Daniel, Wright, Robert, Jonathan O’Muircheartaigh, Baburamani, Ana, Malamateniou, Christina, Hutter, Jana, Edwards, David, Hajnal, Joseph, Rutherford, Mary, Deprez, Maria
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group LTD 01.07.2023
• Subjects: Automation; Bioinformatics; Biometrics; Control Groups; Craniofacial growth; Down syndrome; Down's syndrome; Fetuses; Hypoplasia; Itk protein; Magnetic resonance imaging; Meta Analysis; Neonates; Pediatrics; Phenotyping; Propagation; Ultrasonic imaging; Ultrasound; Variance analysis
• DOI: 10.1136/archdischild-2023-rcpch.249
• ISSN: 0003-9888

ObjectivesPrenatal characterisation of craniofacial development remains a challenge for ultrasound.¹ We sought to develop an MRI protocol for the automated extraction of craniofacial measurements using 3D motion-corrected, slice-to-volume reconstructed (SVR) fetal MRI² and atlas-based label propagation of anatomical landmarks.Methods24 fetuses with genetically confirmed Down syndrome (DS) and 85 control fetuses were retrospectively selected if: scanned between 29–37 weeks GA; had maternal written informed consent via; fetal MRI [REC:07/H0707/105], dHCP [REC:14/LO/1169], PiP [REC:16/LO/1573], eBIDS [REC:19/LO/0667], or iFIND [REC:14/LO/1806]; had a 1.5T or 3T MRI protocol amenable to SVR from 2D acquisitions; and, if the reconstruction quality score was ‘good/excellent’.Using a control dataset, 4D spatiotemporal atlases were developed for 16 discrete time-points from 21–36 weeks GA range.³ A clinician reviewed the literature and 46 fetal MRI-reliable craniofacial landmarks for biometry were labelled in three atlases using research software (ITK-SNAP).4 The label propagation pipeline was followed by the calculation of the distances between selected landmark centre-points. The performance was tested on five datasets with DS, by comparing manual measurements to the automated distances. Lastly, we investigated the feasibility of this approach by comparing the automated DS biometry to the control groups with different acquisition protocols (fig 1).ResultsThe automated craniofacial anatomical landmarks were visually assessed for accuracy. No landmarks in the control group required modification. However, in the DS group 4 out of 120 automated landmarks required minor manual adjustment.Automated biometry, compared to manual measurements, showed small mean paired relative errors of <10%, except for the foramen magnum measurements (figure 2). The differences were primarily caused by variability in multiplanar manual adjustment of images and suboptimal regional visibility of finer features. The process of verifying correct positioning of landmarks was significantly faster than extracting manual biometry (5 vs 25 minutes/case).There were no significant differences in measurements within the control cohorts and between different acquisition parameters (1.5T, 3T; TE=80ms, TE=180ms, TE=250ms – see figure 2). However, there were significant differences between DS and control cohorts in the OFD, ASBL and HPL distances (ANOVA, p<0.001). These differences are likely associated with shorter/wider skulls (brachycephaly) and smaller mid-facies (midface hypoplasia) in DS, which is consistent with ultrasound and neonatal findings.5ConclusionWe present the first automated atlas-based label propagation protocol using 3D motion-corrected MRI for 12 fetal craniofacial measurements across varied echo times and field strengths. The method shows differences in craniofacial growth between fetuses with Down syndrome and control subjects.ReferencesMak ASL, Leung KY. Prenatal ultrasonography of craniofacial abnormalities. Ultrasonography 2019. doi:10.14366/usg.18031Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal J V., Schnabel JA. Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal 2012; 16: 1550.Uus A, Matthew J, Grigorescu I, Jupp S, Grande LC, Price A, et al. Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2021; 12959 LNCS: 168–178.ITK-SNAP tool. http://www.itksnap.org/pmwiki/pmwiki.phpVicente A, Bravo-González LA, López-Romero A, Muñoz CS, Sánchez-Meca J. Craniofacial morphology in down syndrome: a systematic review and meta-analysis. Sci Reports 2020 101 2020;10:1–14.Abstract 560 Figure 1