Landmark-guided region-based spatial normalization for functional magnetic resonance imaging.
Landmark-guided region-based spatial normalization for functional magnetic resonance imaging.
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DOI：
10.1002/hbm.25865
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发表时间：
2022-08-01
影响因子：
4.8
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中科院分区：
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随着用于解决认知神经科学、认知衰老和神经退行性疾病领域的关键问题的神经成像队列的规模的增加，作为必要的预处理步骤的空间归一化的准确性变得极其重要。现有的空间归一化方法在处理高度复杂的人类大脑皮层和当大脑形态发生严重变化(例如，老龄化人口)时，准确率很低。针对这一不足，我们提出了一种新的空间归一化技术，该技术利用现有的基于表面的人脑分割来自动识别和匹配区域地标。为了简化非线性的全脑配准，在保持拓扑变形的情况下，将识别出的每个区域及其对应区域的标志点独立配准。然后，通过反距离加权内插技术将区域的翘曲场组合在一起，从而得到整个大脑的全局翘曲场。为了确保最终的翘曲场是拓扑保持的，我们同时使用具有一定对称约束的正向和反向映射来产生双射性。我们已经使用模拟和真实(结构和功能)的人脑图像来评估我们提出的解决方案。我们的评估表明，与现有方法相比，我们的解决方案可以增强结构一致性。这种改进还提高了功能成像研究的敏感性和特异性，减少了所需的受试者数量和随后的研究成本。我们的结论是，我们提出的解决方案可以有效地替代现有的不符合标准的空间归一化方法来处理目前临床和老龄化研究中常见的大队列的需求。我们提出了一种新的神经成像数据的空间归一化解决方案，特别是功能磁共振成像(FMRI)，该方案利用现有的基于表面的分割技术来执行自动标志点检测和匹配，以便后续基于区域的体积配准。将每个大脑区域独立获得的区域非线性翘曲场组合在一起，生成单个全局翘曲场，并增强拓扑保持属性。我们表明，与TOP-预成型现有方法相比，我们的解决方案提高了大脑区域的结构一致性，并在组级别的激活统计上提高了fMRI激活的敏感性和特异性。
As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential preprocessing step becomes extremely important. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g., aging populations). To address this shortcoming, we propose a novel spatial normalization technique that takes advantage of the existing surface‐based human brain parcellation to automatically identify and match regional landmarks. To simplify the nonlinear whole brain registration, the identified landmarks of each region and its counterpart are registered independently with topology‐preserving deformation. Next, the regional warping fields are combined by an inverse distance weighted interpolation technique to have a global warping field for the whole brain. To ensure that the final warping field is topology‐preserving, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed solution using both simulated and real (structural and functional) human brain images. Our evaluation shows that our solution can enhance structural correspondence compared to the existing methods. Such improvement also increases the sensitivity and specificity of the functional imaging studies, reducing the required number of subjects and subsequent study costs. We conclude that our proposed solution can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts which is now common in clinical and aging studies. We propose a novel spatial normalization solution for neuroimaging data particularly functional magnetic resonance imaging (fMRI), which takes advantage of existing surface‐based parcellation technique to perform automatic landmark detection and matching for subsequent region‐based volumetric registration. The regional non‐linear warping fields obtained independently for each brain region are combined, to generate a single global warping field and enforce topology‐preserving properties. We showed that our solution improves the structural correspondence of brain regions in comparison to the top‐preforming existing methods, and also improves the sensitivity and specificity of the fMRI activation at the group‐level activation statistics.