Kings College London, Department of Neuroimaging, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, United Kingdom.
J Neurosci Methods. 2013 May 30;216(1):62-77. doi: 10.1016/j.jneumeth.2013.03.015. Epub 2013 Apr 1.
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.
脑影像学研究在临床前人群中的规模和复杂性不断增加，迫切需要自动化的图像分析管道。临床前人群可以接受受控干预（例如，靶向损伤），这会显著改变通过成像获得的大脑外观。现有的配准系统（将扫描系统性地对齐到一致的解剖坐标系中）假设与参考扫描的图像相似，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，该步骤在最近描述复杂技术（例如流形学习）的文献中被认为是一种有效方法。因此，在本文中，我们提出了一种使用群体图形模型的仿射配准解决方案，该解决方案将困难的成对配准分解为使用群体中其他成员组成的步骤。我们在中风的临床前模型的背景下开发了这种方法，其中大的、变化的高信号病变显著影响配准性能。我们在模拟的人脑肿瘤图像人群中系统地测试了该技术，然后将其应用于帕金森病和中风的临床前模型。
