Rigid and Nonrigid Registration Methods for Medical Images
by
Justin Wan
University of Waterloo
Coauthors: Lin Xu and Zhao Yi

In image guided procedures such as radiation therapies and computer-assisted surgeries, physicians often need to align images that are taken at different times or by different modalities. Typically, a rigid registration is performed first, followed by a nonrigid registration. We are interested in 2D-3D registration which align digitally reconstructed radiographs generated from 3D datasets (e.g. CT volume from pre-operative planning) with 2D portal image slices (e.g. X-ray image collected in real-time). It is a very computationally intensive procedure. In this talk, we propse fast models that can achieve real-time performance. Afterwards, nonrigid registration needs to be used to enhance the results. Elastic and fluid models were usually used but edges and small details often appear smeared in the transformed templates. In this talk, we also propose a new inviscid model formulated in a particle framework. We will derive the corresponding nonlinear partial differential equations for computing the spatial transformation. Our idea is to simulate the template image as a set of free particles moving toward the target positions under applied forces. Our model can accommodate both small and large deformations, with sharper edges and clear texture achieved at less computational cost. We demonstrate the performance of our model on a variety of images including 2D and 3D, mono-modal and multi-modal, synthetic and clinical data.

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Date received: May 12, 2008