For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.

中文翻译：

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使用高斯过程的基于特征的非刚性图像配准研究

对于广泛的临床应用，例如自适应治疗计划或术中图像更新，基于特征的可变形配准 (FDR) 方法因其简单性和低计算复杂性而被广泛采用。FDR 算法通过插入稀疏场来估计密集位移场，稀疏场由所选特征之间建立的对应关系给出。在本文中，我们将变形场视为高斯过程（GP），而将所选特征视为有效变形的先验信息。使用 GP，我们能够同时估计密集位移场和相应的不确定性图。此外，我们分别使用合成、幻像和临床数据评估了平方指数内核的不同超参数设置的性能。定量比较表明，基于 GP 的插值具有与最先进的 B 样条插值相当的性能。基于 GP 的插值的最大临床益处是它提供了计算密集位移图的数学不确定性的可靠估计。