Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

Zheng, Guoyan (2011). Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration. In: Fichtinger, Gabor; Martel, Anne; Peter, Terry (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. 14th International Conference. Lecture Notes in Computer Science: Vol. 6892 (pp. 598-606). Berlin: Springer 10.1007/978-3-642-23629-7_73

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This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Zheng, Guoyan

ISSN:

0302-9743

ISBN:

978-3-642-23629-7

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:16

Last Modified:

12 Dec 2014 04:16

Publisher DOI:

10.1007/978-3-642-23629-7_73

Web of Science ID:

000307197400073

BORIS DOI:

10.7892/boris.4673

URI:

https://boris.unibe.ch/id/eprint/4673 (FactScience: 209239)

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