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Cross-Manifold Guidance in Deformable Registration of Brain MR Images

机译:脑MR图像可变形登记中的交叉歧管引导

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Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.
机译:歧管通常用于表征个体大脑MR图像的高维分布。变形场,用于注册与模板对象,被感知为在歧管上的图像之间的短程线路径。一般来说,它是不平凡的直接估计变形途径由于歧管的固有的复杂性。在这项工作中,我们打破单一和复杂的歧管的限制,通过短路从多个简单的歧管路径的主题模板途径。具体而言,我们减少了主题/模板图像的解剖复杂性,并把它们投射到虚拟和简化歧管。投影图像的简单引导,然后将被摄体图像来完成其向模板图像空间一步步的旅程。在最后的,受试者模板途径是由横穿较低复杂度的多个歧管,而不是仅依赖于原单一复合歧管来计算。我们验证了交叉歧管的指导,并将其应用于脑MR图像配准。我们认为我们的方法带来更出色的对准精度相比,国家的最先进的可变形配准技术。

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