TY - CHAP
T1 - Multiscale unbiased diffeomorphic atlas construction on Multi-GPUs
AU - Ha, Linh
AU - Krüger, Jens
AU - Joshi, Sarang
AU - Silva, Cláudio T.
N1 - Funding Information:
This research has been funded by the National Science Foundation grant CNS-0751152 and National Institute of Health Grants R01EB007688 and P41-RR023953. L. Ha was partially supported by the Vietnam Education Foundation fellowship. Specially, the authors thank Sam Preston, Marcel Prastawa, and Thomas Fogal for their time and feedback on the work.
PY - 2011
Y1 - 2011
N2 - This chapter presents a high-performance multiscale 3D image-processing framework to exploit the parallel processing power of multiple graphic processing units (multi-GPUs) for medical image analysis. The construction of population atlases plays a central role in medical image analysis, particularly in understanding the variability of brain anatomy. The method projects a large set of images to a common coordinate system, creating a statistical average model of the population, and doing regression analysis of anatomical structures. The brain atlas construction is a powerful technique to study the physiology, evolution, and development of the brain, as well as disease progression. Two desired properties of the atlas construction are that it should be diffeomorphic and nonbiased. GPUs algorithms and data structures can be applied to a wide range of 3D image-processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. The framework helps scientists solve computationally intensive problems that previously required supercomputing power. This chapter illustrates that it is possible to implement unbiased greedy iterative atlas construction on multi-GPUs. Also the framework allows implementation of more sophisticated registration problems, such as LDDMM, metamorphosis, or image current. Although each technique has a different trade-offbetween quality of results and the computation involved, the framework is capable of quantifying those trade-offs to suggest a good solution for the practical problem suitable with inputs and the accessible computational power. © 2011
AB - This chapter presents a high-performance multiscale 3D image-processing framework to exploit the parallel processing power of multiple graphic processing units (multi-GPUs) for medical image analysis. The construction of population atlases plays a central role in medical image analysis, particularly in understanding the variability of brain anatomy. The method projects a large set of images to a common coordinate system, creating a statistical average model of the population, and doing regression analysis of anatomical structures. The brain atlas construction is a powerful technique to study the physiology, evolution, and development of the brain, as well as disease progression. Two desired properties of the atlas construction are that it should be diffeomorphic and nonbiased. GPUs algorithms and data structures can be applied to a wide range of 3D image-processing applications and efficiently exploit the computational power and massive bandwidth offered by modern GPUs. The framework helps scientists solve computationally intensive problems that previously required supercomputing power. This chapter illustrates that it is possible to implement unbiased greedy iterative atlas construction on multi-GPUs. Also the framework allows implementation of more sophisticated registration problems, such as LDDMM, metamorphosis, or image current. Although each technique has a different trade-offbetween quality of results and the computation involved, the framework is capable of quantifying those trade-offs to suggest a good solution for the practical problem suitable with inputs and the accessible computational power. © 2011
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U2 - 10.1016/B978-0-12-384988-5.00048-6
DO - 10.1016/B978-0-12-384988-5.00048-6
M3 - Chapter
AN - SCOPUS:84884437423
SN - 9780123849885
SP - 771
EP - 791
BT - GPU Computing Gems Emerald Edition
PB - Elsevier Inc.
ER -