Matthew Jacqueline, Uus Alena, Egloff Collado Alexia, Luis Aysha, Arulkumaran Sophie, Fukami-Gartner Abi, Kyriakopoulou Vanessa, Cromb Daniel, Wright Robert, Colford Kathleen, Deprez Maria, Hutter Jana, O'Muircheartaigh Jonathan, Malamateniou Christina, Razavi Reza, Story Lisa, Hajnal Joseph V, Rutherford Mary A
Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.
PLOS Digit Health. 2024 Dec 30;3(12):e0000663. doi: 10.1371/journal.pdig.0000663. eCollection 2024 Dec.
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.
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.
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.
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.
产前评估颅面表型与基因型的相关性变得越来越重要；然而，使用三维超声进行评估具有主观性且具有挑战性。我们开发了一种自动标签传播管道，使用三维运动校正、切片到体积重建（SVR）的胎儿磁共振成像（MRI）进行颅面测量。
通过文献综述和专家共识确定了31项用于胎儿MRI的颅面生物测量指标。一个具有明确解剖标志的MRI图谱用作受试者配准、自动标记和生物测量计算的模板。我们评估了108名健康对照者和24名孕晚期（孕龄29 - 36周）的唐氏综合征（T21）胎儿，以确定T21中有意义的生物测量指标。由四名观察者在10个随机数据集中评估可靠性和可重复性。
为所有132名受试者生成了自动标签，放置错误率为0.3%。包括颅底前长度和上颌长度在内的七项测量结果显示，T21组和对照组之间存在显著差异，效应量较大（方差分析，p<0.001）。手动测量每个病例需要25 - 35分钟，而自动提取大约需要5分钟。Bland-Altman图显示，除下颌宽度变异性较高外，在手动观察者范围内具有一致性。基于280名对照胎儿生成了扩展孕龄生长图表（19 - 39周），以供未来研究使用。
这是首个基于图谱的自动方案，使用三维SVR MRI进行胎儿颅面生物测量，准确揭示了T21队列中的颅面形态差异。未来的工作应集中在提高测量可靠性、扩大临床队列以及技术进步方面，以加强产前护理和表型特征描述。
