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Temporal Groupwise Registration for Motion Modeling

机译:运动建模的按时间分组

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We propose a novel method for the registration of time-resolved image sequences, called Spatio-Temporal grOupwise non-rigid Registration using free-form deforMations (STORM). It is a groupwise registration method, with a group of images being considered simultaneously, in order to prevent bias introduction. This is different from pairwise registration methods where only two images are registered to each other. Furthermore, STORM is a spatio-temporal registration method, where both, the spatial and the temporal information are utilized during the registration. This ensures the smoothness and consistency of the resulting deformation fields, which is especially important for motion modeling on medical data. Moreover, popular free-form deformations are applied to model the non-rigid motion. Experiments are conducted on both synthetic and medical images. Results show the good performance and the robustness of the proposed approach with respect to outliers and imaging artifacts, and moreover, its ability to correct for larger deformation in comparison to standard pairwise techniques.
机译:我们提出了一种用于时间分辨图像序列的配准的新方法,称为使用时空变形(STORM)的时空逐行非刚性配准。这是一种逐组套准方法,同时考虑一组图像,以防止引入偏差。这与成对注册方法不同,在成对注册方法中,只有两个图像相互注册。此外,STORM是一种时空配准方法,其中在配准期间同时使用空间和时间信息。这样可以确保所得变形场的平滑性和一致性,这对医学数据的运动建模尤为重要。此外,将流行的自由形式变形应用于非刚性运动的模型。对合成图像和医学图像均进行了实验。结果表明,所提出的方法在离群值和成像伪像方面具有良好的性能和鲁棒性,此外,与标准成对技术相比,它具有校正较大变形的能力。

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