Validation of a nonrigid registration error detection algorithm using clinical MRI brain data.
Validation of a nonrigid registration error detection algorithm using clinical MRI brain data.
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DOI：
10.1109/tmi.2014.2344911
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发表时间：
2015-01
影响因子：
10.6
通讯作者：
Dawant BM
中科院分区：
文献类型：
--
作者：
Datteri RD;Liu Y;D'Haese PF;Dawant BM
非刚性配准中的误差识别是医学图像处理领域的一个关键问题。我们最近提出了一种算法，我们称之为“使用图像配准电路评估质量”（AQUIRC），以识别非刚性配准错误，并使用模拟情况下测试其性能。在这篇文章中，我们扩展了我们以前的工作，以评估AQUIRC的能力，检测局部非刚性配准错误，并在特定的临床标志，即前连合（AC）和后连合（PC）进行定量验证。为了在具有代表性的误差范围内测试我们的方法，我们使用了5种不同的配准方法，并使用了100张目标图像和9张图谱图像。我们的研究结果表明，AQUIRC的配准质量的措施与真实的目标配准误差（TRE）在这些选定的地标与R2 = 0.542。为了将我们的方法与更传统的方法进行比较，我们计算了局部归一化相关系数（LNCC），并表明AQUIRC的性能相似。然而，与AQUIRC的措施和LNCC进行的多元线性回归显示出更高的相关性与TRE的相关性比任何单独的措施，从而显示出这些质量措施的互补性。最后，我们的文章表明，AQUIRC算法可以用来减少所有五种算法的配准误差。
Identification of error in non-rigid registration is a critical problem in the medical image processing community. We recently proposed an algorithm that we call “Assessing Quality Using Image Registration Circuits” (AQUIRC) to identify non-rigid registration errors and have tested its performance using simulated cases. In this article, we extend our previous work to assess AQUIRC’s ability to detect local non-rigid registration errors and validate it quantitatively at specific clinical landmarks, namely the Anterior Commissure (AC) and the Posterior Commissure (PC). To test our approach on a representative range of error we utilize 5 different registration methods and use 100 target images and 9 atlas images. Our results show that AQUIRC’s measure of registration quality correlates with the true target registration error (TRE) at these selected landmarks with an R2 = 0.542. To compare our method to a more conventional approach, we compute Local Normalized Correlation Coefficient (LNCC) and show that AQUIRC performs similarly. However, a multi-linear regression performed with both AQUIRC’s measure and LNCC shows a higher correlation with TRE than correlations obtained with either measure alone, thus showing the complementarity of these quality measures. We conclude the article by showing that the AQUIRC algorithm can be used to reduce registration errors for all five algorithms.