Registration using sparse free-form deformations

Authors:
Wenzhe Shi;Xiahai Zhuang;Luis Pizarro;Wenjia Bai;Haiyan Wang;Kai-Pin Tung;Philip Edwards;Daniel Rueckert
Affiliations:
Biomedical Image Analysis Group, Imperial College London, UK;Shanghai Advanced Research Institute, Chinese Academy of Sciences, China;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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Abstract

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.