Landmark-referenced voxel-based analysis of diffusion tensor images of the brainstem white matter tracts: application in patients with middle cerebral artery stroke.
Landmark-referenced voxel-based analysis of diffusion tensor images of the brainstem white matter tracts: application in patients with middle cerebral artery stroke.
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DOI：
10.1016/j.neuroimage.2008.09.013
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发表时间：
2009-02-01
期刊：
影响因子：
5.7
通讯作者：
Mori S
中科院分区：
文献类型：
--
作者：
Zhang W;Li X;Zhang J;Luft A;Hanley DF;van Zijl P;Miller MI;Younes L;Mori S
虽然DTI可以提供有关白色物质解剖结构的详细信息，但它还不足以直接量化其可视化的解剖信息。在这项研究中，我们开发并测试了一种新的工具来执行大脑正常化和基于体素的DTI数据分析。对于标准化部分，手动放置的标志确保可视化的白色物质束在人群中得到良好的配准。在该程序中集成了洲际弹道导弹-152空间的标准地标集和将其重新映射到主题数据的接口。标志点放置后，由标志点驱动高弹性非线性大变形几何形态度量标测（LDDMM），以标准化正常受试者的脑干解剖结构。然后，该方法被应用于描绘脑干束异常左侧慢性大脑中动脉（MCA）卒中患者。对照组和患者组之间基于体素的比较确定了病灶同侧皮质脊髓束和病灶对侧小脑脚的异常。我们认为，该工具是有用的区域大脑正常化的患者严重的解剖结构的改变，如中风，脑肿瘤，和肺叶切除术，对他们来说，标准的自动化正常化工具可能无法正常工作。
Although DTI can provide detailed information about white matter anatomy, it is not yet straightforward enough to quantify the anatomical information it visualizes. In this study, we developed and tested a new tool to perform brain normalization and voxel-based analysis of DTI data. For the normalization part, manually placed landmarks ensured that the visualized white matter tracts were well-registered among the populations. A standard landmark set in ICBM-152 space and an interface to remap them to subject data were integrated in the procedure. After landmark placement, highly elastic non-linear Large Deformation Diffeomorphic Metric Mapping (LDDMM) was driven by the landmarks to normalize the brainstem anatomy of normal subjects. The approach was then applied to delineate brainstem tract abnormalities in patients with left chronic Middle Cerebral Artery (MCA) stroke. The voxel-based comparison between control and patient groups identified abnormalities in the ipsilesional corticospinal tract and contralesional cerebellar peduncles. We believe that this tool is useful for regional brain normalization of patients with severe anatomical alterations, such as stroke, brain tumor, and lobectomy, for whom standard automated normalization tools may not work properly.
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DOI：
10.1007/11585978_24
发表时间：
2005-01-01
期刊：
ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS
影响因子：
--
作者：
Allassonnière, S;Trouvé, A;Younes, L
通讯作者：
Younes, L
影响因子：
5.7
作者：
Parker, GJM;Stephan, KE;Turner, R
通讯作者：
Turner, R
影响因子：
5.7
作者：
Poupon, C;Clark, CA;Mangin, JF
通讯作者：
Mangin, JF
影响因子：
5.7
作者：
Genovese, CR;Lazar, NA;Nichols, T
通讯作者：
Nichols, T
影响因子：
5.7
作者：
Pierpaoli, C;Barnett, A;Basser, P
通讯作者：
Basser, P