Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.
Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.
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DOI：
10.1016/s1361-8415(99)80030-9
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发表时间：
1999-12-01
影响因子：
10.9
通讯作者：
Suetens, P
中科院分区：
文献类型：
--
作者：
Maes, F;Vandermeulen, D;Suetens, P
体素强度互信息最大化已被证明是三维医学图像配准的一个非常有效的准则，它允许在各种应用中健壮且准确的多模式图像的全自动配准，而不需要对图像进行分割或其他预处理。在本文中，我们研究了最大化互信息的各种优化方法和多分辨率策略的性能，目的是在匹配大的高分辨率图像时提高配准速度。我们证明了当采用适当的插值法时，互信息是仿射配准参数的连续函数，并导出了它的导数的解析表达式，从而可以用数值精确地估计它的梯度。评价了各种基于多分辨率梯度和非梯度的优化策略，如Powell法、单纯形法、最速下降法、共轭梯度法、拟牛顿法和Levenberg-MarQuardt法，用于脑CT和磁共振图像的配准。与采用两级多分辨率格式的单纯形、共轭梯度和Levenberg-MarQuardt方法相比，在全分辨率下，与Powell方法相比，平均速度提高了3倍，且具有类似的精度且不损失稳健性。大数据集，如256(2)x 128 MR和512(2)x 48 CT图像，可以在
Maximization of mutual information of voxel intensities has been demonstrated to be a very powerful criterion for three-dimensional medical image registration, allowing robust and accurate fully automated affine registration of multimodal images in a variety of applications, without the need for segmentation or other preprocessing of the images. In this paper, we investigate the performance of various optimization methods and multiresolution strategies for maximization of mutual information, aiming at increasing registration speed when matching large high-resolution images. We show that mutual information is a continuous function of the affine registration parameters when appropriate interpolation is used and we derive analytic expressions of its derivatives that allow numerically exact evaluation of its gradient. Various multiresolution gradient- and non-gradient-based optimization strategies, such as Powell, simplex, steepest-descent, conjugate-gradient, quasi-Newton and Levenberg-Marquardt methods, are evaluated for registration of computed tomography (CT) and magnetic resonance images of the brain. Speed-ups of a factor of 3 on average compared to Powell's method at full resolution are achieved with similar precision and without a loss of robustness with the simplex, conjugate-gradient and Levenberg-Marquardt method using a two-level multiresolution scheme. Large data sets such as 256(2) x 128 MR and 512(2) x 48 CT images can be registered with subvoxel precision in