Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs
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 variati...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/572187 |
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| _version_ | 1832549534624907264 |
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| author | Linh Ha Marcel Prastawa Guido Gerig John H. Gilmore Cláudio T. Silva Sarang Joshi |
| author_facet | Linh Ha Marcel Prastawa Guido Gerig John H. Gilmore Cláudio T. Silva Sarang Joshi |
| author_sort | Linh Ha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-adcb8d9a6e1e4d4d995ef35fd540f987 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-adcb8d9a6e1e4d4d995ef35fd540f9872025-02-03T06:11:12ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/572187572187Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUsLinh Ha0Marcel Prastawa1Guido Gerig2John H. Gilmore3Cláudio T. Silva4Sarang Joshi5Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USAScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USAScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USADepartment of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USAScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USAScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USADeformable 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.http://dx.doi.org/10.1155/2011/572187 |
| spellingShingle | Linh Ha Marcel Prastawa Guido Gerig John H. Gilmore Cláudio T. Silva Sarang Joshi Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs International Journal of Biomedical Imaging |
| title | Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs |
| title_full | Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs |
| title_fullStr | Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs |
| title_full_unstemmed | Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs |
| title_short | Efficient Probabilistic and Geometric Anatomical Mapping Using Particle Mesh Approximation on GPUs |
| title_sort | efficient probabilistic and geometric anatomical mapping using particle mesh approximation on gpus |
| url | http://dx.doi.org/10.1155/2011/572187 |
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