Diffeomorphic Brain Registration Under Exhaustive Sulcal Constraints
Diffeomorphic Brain Registration Under Exhaustive Sulcal Constraints
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DOI：
10.1109/tmi.2011.2108665
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发表时间：
2011-06-01
影响因子：
10.6
通讯作者：
Baillet, Sylvain
中科院分区：
文献类型：
--
作者：
Auzias, Guillaume;Colliot, Olivier;Baillet, Sylvain
个体大脑结构的对齐和规范化是结构和功能神经成像数据群体水平分析的先决条件。目前可用的技术要么基于体积和/或表面属性，对个体解剖标志的一致对齐的见解有限。这篇文章详细介绍了一个全局的，几何的方法，执行穷尽沟印记对齐（皮质折叠模式）跨个体。这种基于脑沟的皮层（DISCO）技术从t1加权磁共振图像（MRI）序列中自动提取、识别和简化脑沟特征。然后将这些特征用作全三维微分形变形的控制措施。定量和定性评价表明，DISCO正确地排列了个体之间的沟褶和灰质和白质体积。与最近的一种标志性的差形方法（DARTEL）的比较强调了缺乏明确的皮层标志如何导致皮质沟的错位。我们还以DISCO为特色，从群体数据中自动设计一个经验的沟模板。我们还演示了DISCO如何有效地与基于图像的变形（DARTEL）相结合，以进一步提高对准性能的一致性和准确性。最后，我们说明了在群体水平的神经成像数据分析中，跨受试者皮质褶皱的优化排列如何提高功能激活检测的灵敏度。
The alignment and normalization of individual brain structures is a prerequisite for group-level analyses of structural and functional neuroimaging data. The techniques currently available are either based on volume and/or surface attributes, with limited insight regarding the consistent alignment of anatomical landmarks across individuals. This article details a global, geometric approach that performs the alignment of the exhaustive sulcal imprints (cortical folding patterns) across individuals. This DIffeomorphic Sulcal-based COrtical (DISCO) technique proceeds to the automatic extraction, identification and simplification of sulcal features from T1-weighted Magnetic Resonance Image (MRI) series. These features are then used as control measures for fully-3-D diffeomorphic deformations. Quantitative and qualitative evaluations show that DISCO correctly aligns the sulcal folds and gray and white matter volumes across individuals. The comparison with a recent, iconic diffeomorphic approach (DARTEL) highlights how the absence of explicit cortical landmarks may lead to the misalignment of cortical sulci. We also feature DISCO in the automatic design of an empirical sulcal template from group data. We also demonstrate how DISCO can efficiently be combined with an image-based deformation (DARTEL) to further improve the consistency and accuracy of alignment performances. Finally, we illustrate how the optimized alignment of cortical folds across subjects improves sensitivity in the detection of functional activations in a group-level analysis of neuroimaging data.