Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs
Year: 2011
Type: article
Abstract: 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.
DOI: 10.1155/2011/572187
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
Funders: National Science Foundation, National Institutes of Health, University of California, San Francisco, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G229738346, G2431482247, Prospective Studies of the Pathogenesis of Schizophrenia, National Alliance for Medical Image Computing (NAMIC), G4411643199, G4847335975, G6308554035, CRI: IAD: A Service-Oriented Architecture for The Computation, Visualization, and Management of Scientific Data, Online Tools for Customizable ADNI Atlases: Technology and Application, G9000086741, G9000147613
OpenAlex ID: W1998523279