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Cross-Manifold Guidance in Deformable Registration of Brain MR Images

机译:跨歧管引导的脑MR图像的可变形配准

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摘要

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:流形通常用于表征单个大脑MR图像的高维分布。用来将对象与模板对齐的变形场被视为流形上图像之间的测地路径。通常,由于歧管的内在复杂性,直接估计变形路径并非易事。在这项工作中,我们通过将主题模板路径与来自多个更简单歧管的路径短路,打破了单个歧管和复杂歧管的限制。具体而言,我们降低了主题/模板图像的解剖复杂性,并将它们投影到虚拟的和简化的歧管上。然后,投影的简单图像将引导主题图像逐步完成其朝向模板图像空间的过程。最后,主题模板路径是通过遍历较低复杂度的多个流形来计算的,而不是仅依赖于原始的单个复杂流形。我们验证跨歧管的指导,并将其应用于脑MR图像配准。我们得出的结论是,与最先进的可变形套准技术相比,我们的方法可带来更高的对准精度。

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  • 会议地点 Bern(CH)
  • 作者单位

    Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China;

    Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China;

    Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China;

    Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China;

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