Assessing Accuracy of Non-linear Registration in 4D Image Data using Automatically Detected Landmark Correspondences

4D imaging becomes increasingly important in clinical practice. Its use in diagnostics and therapy planning usually requires the application of non-linear registration techniques. The reliability of information derived from the computed transformations is directly dependent on the registration accuracy. Ideally, this accuracy should be evaluated on a patient- and data-specific level – which requires appropriate evaluation criteria and procedures. A standard approach for evaluation of non-linear registration accuracy is to compute a landmark- or point-based registration error by means of manually detected landmark correspondences in the images to register, with the landmarks being anatomically characteristic points. Manual detection of such points is, however, time-consuming and error-prone. In this contribution, different operators for automatic landmark detection and a block matching strategy for landmark propagation in 4D image sequences (here: 4D lung CT, 4D liver MRT) are proposed and evaluated. It turns out that the so-called Förstner-Rohr operators perform best for detection of anatomically characteristic points and that the proposed propagation strategy ensures a robust transfer of these landmarks between the images. The automatically detected landmark correspondences are then used to evaluate the accuracy of different registration approaches (in total 48 variants) applied for registering 4D lung CT data. The resulting registration error values are compared to errors obtained by manually detected landmark pairs. It is shown that derived statements concerning differences in accuracy of the registration approaches are identical for both the manually and the automatically detected landmark sets