Fidon Lucas, Viola Elizabeth, Mufti Nada, David Anna L, Melbourne Andrew, Demaerel Philippe, Ourselin Sébastien, Vercauteren Tom, Deprest Jan, Aertsen Michael
School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK.
Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, WC1E 6DB, UK.
Open Res Eur. 2022 Aug 31;1:123. doi: 10.12688/openreseurope.13914.2. eCollection 2021.
Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA.
开放性脊柱裂(SBA)是一种与发育中的胎儿大脑严重解剖结构变化相关的出生缺陷。脑磁共振成像(MRI)图谱是研究脑解剖结构中神经病理学的常用工具,但以往的胎儿脑MRI图谱主要关注正常胎儿脑。我们旨在开发一种用于SBA的时空胎儿脑MRI图谱。我们开发了一种半自动计算方法来计算首个用于SBA的时空胎儿脑MRI图谱。我们使用了90例孕周在21至35周之间的SBA胎儿的MRI。对所有检查均获得了各向同性且无运动的3D重建MRI。我们提出了一种用于标注SBA胎儿脑3D MRI中解剖标志的方案,目的是使异常胎儿脑MRI的空间对齐更加稳健。此外,我们提出了一种基于解剖标志的加权广义普罗克拉斯方法用于图谱的初始化。所提出的加权广义普罗克拉斯方法可以处理时间正则化和缺失标注。初始化后,使用基于图像强度和解剖标志的非线性图像配准对图谱进行迭代细化。使用半自动方法将我们的胎儿脑图谱划分为八种组织类型:白质、脑室系统、小脑、脑外脑脊液、皮质灰质、深部灰质、脑干和胼胝体。评分者内变异性分析表明七个解剖标志具有足够的可靠性。我们发现所提出的图谱在自动分割SBA胎儿脑3D MRI方面优于正常胎儿脑图谱。我们公开提供了一个用于SBA的时空胎儿脑MRI图谱,可在此处获取:https://doi.org/10.7303/syn25887675。该图谱可支持未来关于SBA胎儿脑3D MRI自动分割方法的研究。