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Automated template-based brain localization and extraction for fetal brain MRI reconstruction.

机译:基于模板的自动化脑定位和提取,用于胎儿脑MRI重建。

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摘要

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.
机译:大多数胎儿脑MRI重建算法仅依赖于低分辨率(LR)图像的与脑组织相关的体素,以增强切片间运动校正和图像重建的质量。因此,第一步需要对胎儿的大脑进行定位和提取,这通常是费力且费时的手动或半自动任务。我们在这项工作中建议使用年龄匹配的模板图像作为先验知识来自动进行大脑定位和提取。这是通过基于健壮的模板到切片块匹配和可变形的切片到模板配准的新颖的自动大脑定位和提取方法实现的。我们基于模板的方法还可以在公共坐标空间中的标准放射解剖平面中重建胎儿大脑图像。我们已将此方法集成到新的重建流程中,该流程涉及强度归一化,切片间运动校正和超分辨率(SR)重建。为此,我们采用了一种新颖的方法,该方法基于使用融合策略将LR脑罩的每个切片投影到模板空间中的情况。这样就可以在每次运动校正迭代时优化LR图像中的脑罩。整体的大脑定位和提取算法已显示出与手动绘制的脑罩非常接近的脑罩,其平均Dice重叠测量值为94.5%。我们还证明,与全局刚性脑提取相比,采用逐层切片的模板到模板的配准和脑掩模的传播会导致脑提取性能的显着改善,从而最终重建图像的质量也得到改善。两位专家观察员进行的评级显示,该拟议管道可以实现与基于手动逐个切片脑提取的参考重建相似的重建质量。拟议中的脑罩优化和重建方法已显示可在胎龄为23至38周的26例胎儿中进行自动胎儿脑MRI分割和容量测定,提供了有希望的结果。

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