Non-rigid free-form 2D–3D registration using a B-spline-based statistical deformation model - Yu, Weimin; Tannast, Moritz; Zheng, Guoyan (Pattern recognition, 2017)
This paper presents a non-rigid free-from 2D–3D registration approach using a B-spline-based statistical deformation model (SDM). In our approach the B-spline-based SDM is first constructed from a set of training data using a non-rigid registration algorithm based on B-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D–3D registration algorithm is then presented to iteratively fit the 3D B-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast 3D B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Comprehensive experiments on simulated images of patient data sets, on calibrated C-arm and on X-ray images of cadaveric femurs are designed and conducted to evaluate the performance of the proposed approach. The experimental results demonstrate the efficacy of the present approach. •A non-rigid free-from 2D–3D registration approach using a B-spline-based statistical deformation model.•A fast and accurate 3D B-spline pseudo-inversion algorithm.•Comprehensive validation.•Evaluate not only the volume reconstruction accuracy but also the cortical bone region reconstruction accuracy.