School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Guy's and St Thomas' NHS Foundation Trust, London, UK.
BMC Med Imaging. 2024 Mar 1;24(1):52. doi: 10.1186/s12880-024-01230-7.
This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.
这项研究探索了 3D Slice-to-Volume Registration（SVR）运动校正胎儿 MRI 在颅面评估中的潜力，传统上仅用于胎儿大脑分析。此外，我们还介绍了第一个基于 3D 注意力 UNet 的自动化管道的描述，该管道经过训练可用于 3D 胎儿 MRI 颅面分割，然后进行表面细化。还介绍了选定模型的 3D 打印结果。基于 SVR 输出和表面分割输出的多平面体积的定性分析，使用我们为评估图像质量和诊断颅面特征可见性而开发的标准化协议，通过计算机和打印模型进行了评估。我们对 25 例经产后证实的 21 三体胎儿病例（24-36 周妊娠龄）进行了测试集分析，结果表明 3D 重建 T2 SVR 图像在 SVR 输出中提供了 66-100%的相关颅面和头部结构的可见性，而基线和细化的 3D 计算机表面模型输出的解剖可见性分别为 20-100%和 60-90%。此外，25 例病例中有 12 例（48%）细化表面模型的整体质量良好或优秀，另有 9 例（36%）的质量为中等，包括面部、头皮和外耳。另外 12 例物理全尺寸模型（20-36 周妊娠龄）的 3D 打印结果显示，所有病例的整体质量均良好/优秀，并且在正常对照病例和经证实存在异常的病例之间可区分特征，仅需在 3D 打印前进行少量手动调整。尽管存在图像质量和数据异质性，3D T2w SVR 重建和模型仍为颅面细微特征的主观特征化提供了足够的分辨率。我们还提供了一个可公开访问的胎儿头部 3D T2w MRI 图谱，该图谱经过验证可准确表示正常胎儿解剖结构。未来的研究将集中在定量分析、优化管道以及探索胎儿颅面评估中的诊断、咨询和教育应用上。
