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首页> 外文期刊>Medical image analysis >Grid powered nonlinear image registration with locally adaptive regularization.
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Grid powered nonlinear image registration with locally adaptive regularization.

机译：具有局部自适应正则化的网格供电非线性图像配准。

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摘要

Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not sufficient. In this paper, we present an algorithm that uses a priori information about the nature of imaged objects in order to adapt the regularization of the deformations. We also present a robustness improvement that gives higher weight to those points in images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. In order to improve the usability of the parallel software by a clinical user, we have implemented it as a grid service that can be controlled by a graphics workstation embedded in the clinical environment. Results on inter-subject pairs of images show that our method can take into account the large variability of most brain structures. The registration time for images of size 256 x 256 x 124 is 5 min on 15 standard PCs. A comparison of our non-stationary visco-elastic smoothing versus solely elastic or fluid regularizations shows that our algorithm converges faster towards a more optimal solution in terms of accuracy and transformation regularity.
机译：使用密集形变场的多对象非刚性配准算法经常遇到要估计的变换具有较大空间变异性的情况。在这些情况下，线性平稳正则化方法是不够的。在本文中，我们提出一种算法，该算法使用有关成像对象性质的先验信息，以适应变形的正则化。我们还提出了一种鲁棒性改进，可以使包含更多信息的图像中的那些点具有更高的权重。最后，提出了使用联网个人计算机的快速并行实现。为了提高临床用户对并行软件的可用性，我们已将其实现为网格服务，可以通过嵌入在临床环境中的图形工作站进行控制。受试者间图像对的结果表明，我们的方法可以考虑大多数大脑结构的较大变异性。在15台标准PC上，尺寸为256 x 256 x 124的图像的注册时间为5分钟。我们的非平稳粘弹性平滑与单独的弹性或流体正则化的比较表明，我们的算法在准确性和变换规律性方面朝更快的最优解收敛。

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