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A hybrid optimization method for maximum mutual information registration

机译:最大互信息注册的混合优化方法

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Maximum mutual information (MI) is widely used in multimodality registration. However, traditional registration based on MI is difficult to be adopted in clinical routine for its time-consuming. We proposed a hybrid optimization method which can reduce time for one evaluation of the MI criterion by using incremental coordinate transformation method and also can reduce the whole iteration time by using multi-resolution as the search strategy. The performance of the hybrid optimization method was evaluated for rigid-body registration of CT and PET images of the same patient, and the results showed the hybrid method was faster than traditional maximum mutual information method.
机译:最大互信息(MI)广泛用于多模式注册。然而,基于MI的传统注册由于其耗时而难以在临床常规中采用。我们提出了一种混合优化方法,该方法可以通过使用增量坐标变换方法来减少一次对MI准则进行评估的时间,并且可以通过使用多分辨率作为搜索策略来减少整个迭代时间。对同一患者的CT和PET图像进行刚体配准,评估了混合优化方法的性能,结果表明该混合方法比传统的最大互信息方法更快。

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