Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

Zheng, Guoyan; Yu, Weimin (2015). Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model. In: Zhou, Luping; Wang, Li; Wang, Qian; Shi, Yinghuan (eds.) Machine Learning in Medical Imaging. 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings 9352 (pp. 102-109). Cham: Springer 10.1007/978-3-319-24888-2_13

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This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the 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 B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

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