Department of Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany.
IEEE Trans Med Imaging. 2009 Nov;28(11):1708-16. doi: 10.1109/TMI.2009.2021063. Epub 2009 May 12.
Uncertainties in image registration may be a significant source of errors in anatomy mapping as well as dose accumulation in radiotherapy. It is, therefore, essential to validate the accuracy of image registration. Here, we propose a method to detect areas where mono modal B-spline registration performs well and to distinguish those from areas of the same image, where the registration is likely to be less accurate. It is a stochastic approach to automatically estimate the uncertainty of the resulting displacement vector field. The coefficients resulting from the B-spline registration are subject to moderate and randomly performed variations. A quantity is proposed to characterize the local sensitivity of the similarity measure to these variations. We demonstrate the statistical dependence between the local image registration error and this quantity by calculating their mutual information. We show the significance of the statistical dependence with an approach based on random redistributions. The proposed method has the potential to divide an image into subregions which differ in the magnitude of their average registration error.
图像配准中的不确定性可能是解剖映射以及放射治疗中剂量积累的一个重要误差源。因此，验证图像配准的准确性是至关重要的。在这里，我们提出了一种方法来检测单模态 B 样条配准表现良好的区域，并将其与配准可能不太准确的同一图像区域区分开来。这是一种随机方法，可以自动估计所得位移矢量场的不确定性。B 样条配准的系数受到适度的和随机执行的变化的影响。提出了一个数量来描述相似性度量对这些变化的局部敏感性。我们通过计算它们的互信息来证明局部图像配准误差与该数量之间的统计相关性。我们通过基于随机重新分配的方法来证明统计相关性的重要性。所提出的方法有可能将图像分成子区域，这些子区域在其平均配准误差的大小上有所不同。
