Quantitative evaluation of image-based distortion correction in diffusion tensor imaging
Quantitative evaluation of image-based distortion correction in diffusion tensor imaging
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DOI：
10.1109/tmi.2004.827479
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发表时间：
2004-07-01
影响因子：
10.6
通讯作者：
van Muiswinkel, A
中科院分区：
文献类型：
--
作者：
Netsch, T;van Muiswinkel, A
提出了一种基于配准结果一致性评价的图像序列配准统计评价方法。一致性定义为循环配准合成的残差。通过组合不同算法的变换，一致性误差允许在不使用地面实况的情况下进行定量比较，具体地说，它允许确定算法是否兼容并因此提供可比较的配准。一致性检验被应用于评价脑扩散张量成像中涡流诱导的图像失真的回顾性校正。在文献中，已经提出了几种图像变换和相似性度量，通常显示出在配准前后的参数图的并排比较中失真的显著减少。变换来自成像物理和三维仿射变换以及互信息（MI）和局部相关（LC）的相似性进行了比较，通过一致性测试。对于所考虑的一半以上的系列，专用转换无法证明显著差异。LC相似性非常适合于失真校正，提供与MI相当的更一致的配准。
A statistical method for the evaluation of image registration for a series of images based on the assessment of consistency properties of the registration results is proposed. Consistency is defined as the residual error of the composition of cyclic registrations. By combining the transformations of different algorithms the consistency error allows a quantitative comparison without the use of ground truth, specifically, it allows a determination as to whether the algorithms are compatible and hence provide comparable registrations. Consistency testing is applied to evaluate retrospective correction of eddy current-induced image distortion in diffusion tensor imaging of the brain. In the literature several image transformations and similarity measures have been proposed, generally showing a significant reduction of distortion in side-by-side comparison of parametric maps before and after registration. Transformations derived from imaging physics and a three-dimensional affine transformation as well as mutual information (MI) and local correlation (LC) similarity are compared to each other by means of consistency testing. The dedicated transformations could not demonstrate a significant difference for more than half of the series considered. LC similarity is well-suited for distortion correction providing more consistent registrations which are comparable to MI.