PDF(Pubmed)
Abstract：
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.
摘要：
这项研究探讨了3D切片体积配准（SVR）运动校正胎儿MRI用于颅面评估的潜力，传统上只用于胎儿大脑分析。此外,我们介绍了基于3D注意力UNet的自动管道的第一个描述，用于3D胎儿MRI颅面分割，其次是表面细化。还介绍了选定模型的3D打印结果。多平面体积的定性分析，基于SVR输出和曲面分割输出，用电脑和打印模型评估，使用我们开发的标准化协议来评估诊断颅面特征的图像质量和可见性。一套25人的测试，产后确认，胎儿三体21例（胎龄24-36周），显示3D重建的T2SVR图像在SVR输出中提供了相关颅面和头部结构的66-100％的可见性，在基线和精细的3D计算机表面模型输出中,解剖可见性分别为20-100%和60-90%.此外，25个案例中的12个，48%,精炼的表面模型在另外9个案例中表现出良好或优异的整体质量，36%,表现出中等质量，包括面部，头皮和外耳。对12个实际尺寸模型（胎龄20-36周）进行额外的3D打印，在所有情况下都显示出良好/出色的整体质量，并且在健康对照病例和确诊异常病例之间具有明显的特征。在3D打印之前只需要进行少量的手动调整。尽管图像质量和数据异质性不同，3DT2wSVR重建和模型为微妙的颅面特征的主观表征提供了足够的分辨率。我们还提供了一个可公开访问的在线3DT2w胎儿头部MRI图谱，验证了正常胎儿解剖结构的准确表示。未来的研究将集中在定量分析上，优化管道，探索诊断，咨询,以及在胎儿颅面评估中的教育应用。
