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.
@article{arxiv.2001.05862,
title = {An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process},
author = {Siming Bayer and Ute Spiske and Jie Luo and Tobias Geimer and William M. Wells and Martin Ostermeier and Rebecca Fahrig and Arya Nabavi and Christoph Bert and Ilker Eyupoglo and Andreas Maier},
journal= {arXiv preprint arXiv:2001.05862},
year = {2020}
}