Tourbier Sébastien, Velasco-Annis Clemente, Taimouri Vahid, Hagmann Patric, Meuli Reto, Warfield Simon K, Bach Cuadra Meritxell, Gholipour Ali
Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA; Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland; Radiology Department, Lausanne University Hospital Center (CHUV) and University of Lausanne (UNIL), Switzerland.
Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
Neuroimage. 2017 Jul 15;155:460-472. doi: 10.1016/j.neuroimage.2017.04.004. Epub 2017 Apr 11.
Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.
大多数胎儿脑磁共振成像(MRI)重建算法仅依赖于低分辨率(LR)图像中与脑组织相关的体素,以提高层间运动校正和图像重建的质量。因此,胎儿脑作为第一步需要进行定位和提取,这通常是一项费力且耗时的手动或半自动任务。在这项工作中,我们提出使用年龄匹配的模板图像作为先验知识,以实现脑定位和提取的自动化。这是通过一种基于稳健的模板到切片块匹配和可变形切片到模板配准的新型自动脑定位和提取方法实现的。我们基于模板的方法还能够在公共坐标空间中以标准放射解剖平面重建胎儿脑图像。我们已将此方法集成到新的重建流程中,该流程包括强度归一化、层间运动校正和超分辨率(SR)重建。为此,我们采用了一种基于使用融合策略将LR脑掩码的每一层投影到模板空间的新方法。这使得在每次运动校正迭代时能够细化LR图像中的脑掩码。整体脑定位和提取算法已证明能够生成与手动绘制的脑掩码非常接近的脑掩码,平均骰子重叠度量为94.5%。我们还证明,与全局刚性脑提取相比,逐片进行切片到模板配准和脑掩码传播可显著提高脑提取性能,从而提高最终重建图像的质量。两位专家观察者进行的评分表明,所提出的流程能够实现与基于手动逐片脑提取的参考重建相似的重建质量。所提出的脑掩码细化和重建方法已证明在对26例孕周范围为23至38周的胎儿进行自动胎儿脑MRI分割和容积测量方面提供了有前景的结果。