Multiscale unbiased diffeomorphic atlas construction on Multi-GPUs

Linh Ha, Jens Krüger, Sarang Joshi, Cláudio T. Silva

Research output: Chapter in Book/Report/Conference proceedingChapter


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

Original languageEnglish (US)
Title of host publicationGPU Computing Gems Emerald Edition
PublisherElsevier Inc.
Number of pages21
ISBN (Print)9780123849885
StatePublished - 2011

ASJC Scopus subject areas

  • General Computer Science


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