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Proceedings Paper

Efficient 3D rigid-body registration of micro-MR and micro-CT trabecular bone images
Author(s): C. S. Rajapakse; J. Magland; S. L. Wehrli; X. H. Zhang; X. S. Liu; X. E. Guo; F. W. Wehrli
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Paper Abstract

Registration of 3D images acquired from different imaging modalities such as micro-magnetic resonance imaging (µMRI) and micro-computed tomography (µCT) are of interest in a number of medical imaging applications. Most general-purpose multimodality registration algorithms tend to be computationally intensive and do not take advantage of the shape of the imaging volume. Multimodality trabecular bone (TB) images of cylindrical cores, for example, tend to be misaligned along and around the axial direction more than that around other directions. Additionally, TB images acquired by µMRI can differ substantially from those acquired by µCT due to apparent trabecular thickening from magnetic susceptibility boundary effects and non-linear intensity correspondence. However, they share very similar contrast characteristics since the images essentially represent a binary tomographic system. The directional misalignment and the fundamental similarities of the two types of images can be exploited to achieve fast 3D registration. Here we present an intensity cross-correlation based 3D registration algorithm for registering 3D specimen images from cylindrical cores of cadaveric TB acquired by µMRI and µCT in the context of finite-element modeling to assess the bone's mechanical constants. The algorithm achieves the desired registration by first coarsely approximating the three translational and three rotational parameters required to align the µMR images to the µCT scan coordinate frame and fine-tuning the parameters in the neighborhood of the approximate solution. The algorithm described here is suitable for 3D rigid-body image registration applications where through-plane rotations are known to be relatively small. The accuracy of the technique is constrained by the image resolution and in-plane angular increments used.

Paper Details

Date Published: 11 March 2008
PDF: 7 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69142Z (11 March 2008); doi: 10.1117/12.770931
Show Author Affiliations
C. S. Rajapakse, Univ. of Pennsylvania School of Medicine (United States)
J. Magland, Univ. of Pennsylvania School of Medicine (United States)
S. L. Wehrli, The Children's Hospital of Philadelphia (United States)
X. H. Zhang, Columbia Univ. (United States)
X. S. Liu, Columbia Univ. (United States)
X. E. Guo, Columbia Univ. (United States)
F. W. Wehrli, Univ. of Pennsylvania School of Medicine (United States)


Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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