Comparison of global and local optimization methods for intensity-based 2D–3D registration

Intensity-based 2D-3D registration methods are commonly used in musculoskeletal research and image-guided therapy to align 2D X-ray images with 3D CT scans. However, their success rate (SR) is limited by local optimization methods, which often cause the optimization of the underlying cost function to get stuck at a local minimum, resulting in false alignments. Global optimization methods aim to mitigate this problem, but despite their increasing popularity, the existing literature lacks consensus on which one is the most appropriate. In this work, we compare 11 global and 4 local optimization methods on thousands of typical registration examples of single- and dual-plane fluoroscopy, including three datasets of varying complexity. In addition, we evaluate the differences between global and local methods, determine the best overall method, and validate its suitability for real clinical data. The results demonstrate that global methods that require a large number of function evaluations (NFEV) are generally the most robust. Furthermore, hyperparameter tuning can significantly improve their performance and is generalizable across datasets. Evolutionary strategy (ES) is the best-performing optimization method in our study, achieving a mean SR of ∼95% for all test models, ∼77% for the knee bones, and ∼95-100% for cerebral angiograms when using dual-plane registration setup. Nevertheless, in cases where good initialization is available, local methods are still preferable due to their reduced NFEV. The use of global optimization improves the overall robustness and ease-of-use of 2D-3D registration, potentially accelerating its adaptation in routine medical practice and biomedical research.