Improved optimization for the robust and accurate linear registration and motion correction of brain images
Improved optimization for the robust and accurate linear registration and motion correction of brain images
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DOI：
10.1006/nimg.2002.1132
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发表时间：
2002-10-01
期刊：
影响因子：
5.7
通讯作者：
Smith, S
中科院分区：
文献类型：
--
作者：
Jenkinson, M;Bannister, P;Smith, S
线性配准和运动校正是结构和功能脑图像分析的重要组成部分。大多数现代方法优化一些基于强度的成本函数以确定最佳配准。迄今为止，很少有人关注优化方法本身，即使大多数配准方法的成功取决于这种优化的质量。本文详细研究了优化过程，并证明了常用的多分辨率局部优化方法可以，而且确实会陷入局部极小值。为了解决这个问题，采取了两种方法：（1）切趾成本函数和（2）采用一种新的混合全局-局部优化方法。这种新的优化方法是专门设计用于配准全脑图像。它大大降低了由于被局部最小值捕获而产生配准不良的可能性。通过一致性试验证明，与其他常用方法相比，该方法的耐用性增加。此外，配准的准确性证明了一系列的实验与运动校正。这些运动校正实验还研究了不同成本函数和插值方法对结果的影响。(C)2002 Elsevier Science（美国）。
Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods. (C) 2002 Elsevier Science (USA).