Splitting proximate algorithm for deformable image registration based on functions of bounded deformation
Chen Li, Ziwei Nie, Xiaoping Yang · Medical Physics · 2022
BACKGROUND: Deformable image registration is a crucial task in the field of medical image analysis. Functions of bounded deformation (BD) have been proved effective for modeling the displacement fields between medical images since they can capture the discontinuity of displacement fields along edges of organs and tissues. PURPOSE: However, we find that at the same time, BD functions-based models tend to obtain discontinuous displacement fields inside the regions of organs and tissues due to image noises in some cases and the presented gradient descent algorithm is time-consuming. To alleviate these problems, we propose a faster algorithm named SPA: splitting proximate algorithm. METHODS: In the framework of variable-splitting scheme, we incorporate a proximal term in the deformable registration energy based on functions of BD. RESULTS: The proposed algorithm can efficiently solve the original model and obtain displacement fields, which look more natural and plausible. Numerical experiments show the effectiveness and stability of the proposed algorithm. CONCLUSIONS: The proposed SPA is able to drastically register the images with a plausible deformation field and not sensitive to noise.