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一种特定于研究的功能磁共振成像(fMRI)归一化方法,该方法直接对7特斯拉的高分辨率功能回波平面成像(EPI)数据进行操作。

A study-specific fMRI normalization approach that operates directly on high resolution functional EPI data at 7 Tesla.

作者信息

Grabner Günther, Poser Benedikt A, Fujimoto Kyoko, Polimeni Jonathan R, Wald Lawrence L, Trattnig Siegfried, Toni Ivan, Barth Markus

机构信息

Radboud University, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; MR Centre of Excellence, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Austria.

Radboud University, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Erwin. L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany; Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands.

出版信息

Neuroimage. 2014 Oct 15;100:710-4. doi: 10.1016/j.neuroimage.2014.06.045. Epub 2014 Jun 25.

Abstract

Due to the availability of ultra-high field scanners and novel imaging methods, high resolution, whole brain functional MR imaging (fMRI) has become increasingly feasible. However, it is common to use extensive spatial smoothing to account for inter-subject anatomical variation when pooling over subjects. This reduces the spatial details of group level functional activation considerably, even when the original data was acquired with high resolution. In our study we used an accelerated 3D EPI sequence at 7 Tesla to acquire whole brain fMRI data with an isotropic spatial resolution of 1.1mm which shows clear gray/white matter contrast due to the stronger T1 weighting of 3D EPI. To benefit from the high spatial resolution on the group level, we develop a study specific, high resolution anatomical template which is facilitated by the good anatomical contrast that is present in the average functional EPI images. Different template generations with increasing accuracy were created by using a hierarchical linear and stepwise non-linear registration approach. As the template is based on the functional data themselves no additional co-registration step with the usual T1-weighted anatomical data is necessary which eliminates a potential source of misalignment. To test the improvement of functional localization and spatial details we performed a group level analysis of a finger tapping experiment in eight subjects. The most accurate template shows better spatial localization--such as a separation of somatosensory and motor areas and of single digit activation--compared to the simple linear registration. The number of activated voxels is increased by a factor of 1.2, 2.5, and 3.1 for somatosensory, supplementary motor area, and dentate nucleus, respectively, for the functional contrast between left versus right hand. Similarly, the number of activated voxels is increased 1.4- and 2.4-fold for right little versus right index finger and left little versus left index finger, respectively. The Euclidian distance between the activation (center of gravity) of the respective fingers was found to be 13.90 mm using the most accurate template.

摘要

由于超高场扫描仪和新型成像方法的出现,高分辨率全脑功能磁共振成像(fMRI)变得越来越可行。然而,在对受试者进行汇总时,通常会使用广泛的空间平滑来考虑受试者间的解剖变异。这会显著降低组水平功能激活的空间细节,即使原始数据是高分辨率采集的。在我们的研究中,我们使用7特斯拉的加速3D EPI序列来采集全脑fMRI数据,其各向同性空间分辨率为1.1毫米,由于3D EPI更强的T1加权,显示出清晰的灰质/白质对比。为了在组水平上受益于高空间分辨率,我们开发了一个特定于研究的高分辨率解剖模板,这得益于平均功能EPI图像中存在的良好解剖对比度。通过使用分层线性和逐步非线性配准方法创建了具有越来越高准确性的不同模板生成。由于模板基于功能数据本身,因此无需与通常的T1加权解剖数据进行额外的共配准步骤,这消除了潜在的错位源。为了测试功能定位和空间细节的改善,我们对八名受试者的手指敲击实验进行了组水平分析。与简单线性配准相比,最准确的模板显示出更好的空间定位,例如体感和运动区域的分离以及单个手指激活的分离。对于左手与右手之间的功能对比,体感、辅助运动区和齿状核的激活体素数量分别增加了1.2倍、2.5倍和3.1倍。同样,右手小指与右手食指以及左手小指与左手食指的激活体素数量分别增加了1.4倍和2.4倍。使用最准确的模板发现,各手指激活(重心)之间的欧几里得距离为13.90毫米。

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