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基于刚性配准方法的全头部弥散光学层析成像研究评估。

Evaluation of rigid registration methods for whole head imaging in diffuse optical tomography.

机构信息

University of Birmingham , School of Computer Science, Edgbaston, Birmingham B15 2TT, United Kingdom.

Washington University School of Medicine , Department of Radiology, 4525 Scott Avenue, St. Louis, Missouri 63110, United States.

出版信息

Neurophotonics. 2015 Jul;2(3):035002. doi: 10.1117/1.NPh.2.3.035002. Epub 2015 Jul 21.

Abstract

Functional brain imaging has become an important neuroimaging technique for the study of brain organization and development. Compared to other imaging techniques, diffuse optical tomography (DOT) is a portable and low-cost technique that can be applied to infants and hospitalized patients using an atlas-based light model. For DOT imaging, the accuracy of the forward model has a direct effect on the resulting recovered brain function within a field of view and so the accuracy of the spatially normalized atlas-based forward models must be evaluated. Herein, the accuracy of atlas-based DOT is evaluated on models that are spatially normalized via a number of different rigid registration methods on 24 subjects. A multileveled approach is developed to evaluate the correlation of the geometrical and sensitivity accuracies across the full field of view as well as within specific functional subregions. Results demonstrate that different registration methods are optimal for recovery of different sets of functional brain regions. However, the "nearest point to point" registration method, based on the EEG 19 landmark system, is shown to be the most appropriate registration method for image quality throughout the field of view of the high-density cap that covers the whole of the optically accessible cortex.

摘要

功能脑成像已成为研究大脑组织和发育的重要神经影像学技术。与其他成像技术相比,漫射光学断层扫描(DOT)是一种便携式且低成本的技术,可使用基于图谱的光模型应用于婴儿和住院患者。对于 DOT 成像,正向模型的准确性直接影响视野内恢复的大脑功能,因此必须评估基于图谱的正向模型的空间归一化准确性。在此,通过对 24 个对象进行多种不同刚性配准方法的空间归一化,评估了基于图谱的 DOT 的准确性。开发了一种多层次方法来评估整个视野内以及特定功能子区域内的几何和灵敏度准确性的相关性。结果表明,不同的配准方法对于恢复不同的功能脑区是最优的。然而,基于 EEG 19 个标记系统的“最近点到点”配准方法被证明是在覆盖整个可光学访问皮层的高密度帽的整个视野中获得最佳图像质量的最适当的配准方法。

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