Registration of challenging pre-clinical brain images.
Registration of challenging pre-clinical brain images.
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DOI：
10.1016/j.jneumeth.2013.03.015
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发表时间：
2013-05-30
影响因子：
3
通讯作者：
Williams, Steven C. R.
中科院分区：
文献类型：
--
作者：
Crum, William R.;Modo, Michel;Vernon, Anthony C.;Barker, Gareth J.;Williams, Steven C. R.
一种用于注册高度可变的脑图像群体的方法。受高度异常外观的临床前图像启发并应用于这些图像。使用不同大小病变的大脑模拟图像进行测试。应用于帕金森病和中风模型人群。临床前人群脑成像研究的规模和复杂性正在增加，迫切需要自动化图像分析管道。临床前人群可以接受受控干预（例如，靶向病变），这显著改变了通过成像获得的脑的外观。现有的配准系统（将扫描系统地对准到一致的解剖坐标系中）假设图像与参考扫描相似，当应用于这些图像时可能会失败。然而，仿射配准是后续图像分析的一个特别重要的预处理步骤，在最近的文献中描述了复杂的技术，如流形学习，这被认为是一个有效的过程。因此，在本文中，我们提出了一种仿射配准解决方案，该方案使用人口的图形模型将困难的成对配准分解为使用人口中其他成员的步骤组合。我们在中风的临床前模型的背景下开发了这种方法，在该模型中，大的、可变的高强度病变显著影响配准性能。在将其应用于帕金森病和中风的临床前模型之前，我们在模拟的人类脑肿瘤图像中系统地测试了这项技术。
A method for registering highly variable brain image populations. Motivated by and applied to pre-clinical images with highly abnormal appearance. Tested using simulated images of brains with lesions of varying sizes. Applied to Parkinson's disease and stroke model populations. The size and complexity of brain imaging studies in pre-clinical populations are increasing, and automated image analysis pipelines are urgently required. Pre-clinical populations can be subjected to controlled interventions (e.g., targeted lesions), which significantly change the appearance of the brain obtained by imaging. Existing systems for registration (the systematic alignment of scans into a consistent anatomical coordinate system), which assume image similarity to a reference scan, may fail when applied to these images. However, affine registration is a particularly vital pre-processing step for subsequent image analysis which is assumed to be an effective procedure in recent literature describing sophisticated techniques such as manifold learning. Therefore, in this paper, we present an affine registration solution that uses a graphical model of a population to decompose difficult pairwise registrations into a composition of steps using other members of the population. We developed this methodology in the context of a pre-clinical model of stroke in which large, variable hyper-intense lesions significantly impact registration performance. We tested this technique systematically in a simulated human population of brain tumour images before applying it to pre-clinical models of Parkinson's disease and stroke.
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影响因子：
5.7
作者：
Badea, Alexandra;Gewalt, Sally;Avants, Brian B.;Cook, James J.;Johnson, G. Allan
通讯作者：
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影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
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作者：
Andersen, Sarah M.;Rapcsak, Steven Z.;Beeson, Pelagie M.
通讯作者：
Beeson, Pelagie M.
影响因子：
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作者：
Gooya A;Biros G;Davatzikos C
通讯作者：
Davatzikos C
DOI：
10.1016/j.compmedimag.2011.09.001
发表时间：
2012-03-01
影响因子：
5.7
作者：
Jia, Hongjun;Wu, Guorong;Shen, Dinggang
通讯作者：
Shen, Dinggang