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International Journal of Biomedical Imaging
Volume 2011, Article ID 572187, 16 pages
Research Article

Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA

Received 2 March 2011; Revised 6 May 2011; Accepted 3 June 2011

Academic Editor: Aly A. Farag

Copyright © 2011 Linh Ha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Deformable image registration in the presence of considerable contrast differences and large size and shape changes presents significant research challenges. First, it requires a robust registration framework that does not depend on intensity measurements and can handle large nonlinear shape variations. Second, it involves the expensive computation of nonlinear deformations with high degrees of freedom. Often it takes a significant amount of computation time and thus becomes infeasible for practical purposes. In this paper, we present a solution based on two key ideas: a new registration method that generates a mapping between anatomies represented as a multicompartment model of class posterior images and geometries and an implementation of the algorithm using particle mesh approximation on Graphical Processing Units (GPUs) to fulfill the computational requirements. We show results on the registrations of neonatal to 2-year old infant MRIs. Quantitative validation demonstrates that our proposed method generates registrations that better maintain the consistency of anatomical structures over time and provides transformations that better preserve structures undergoing large deformations than transformations obtained by standard intensity-only registration. We also achieve the speedup of three orders of magnitudes compared to a CPU reference implementation, making it possible to use the technique in time-critical applications.