Applied numerical linear algebra
Applied numerical linear algebra
Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Stream computing on graphics hardware
Stream computing on graphics hardware
IEEE Transactions on Visualization and Computer Graphics
Unbiased atlas formation via large deformations metric mapping
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Fast deformable registration of 3d-ultrasound data using a variational approach
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Solving the euler equations on graphics processing units
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Image registration driven by combined probabilistic and geometric descriptors
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Fast shape-based nearest-neighbor search for brain MRIs using hierarchical feature matching
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Patient specific dosimetry phantoms using multichannel LDDMM of the whole body
Journal of Biomedical Imaging - Special issue on Parallel Computation in Medical Imaging Applications
Optimal multi-image processing streaming framework on parallel heterogeneous systems
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
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Unbiased diffeomorphic atlas construction has proven to be a powerful technique for medical image analysis, particularly in brain imaging. The method operates on a large set of images, mapping them all into a common coordinate system, and creating an unbiased common template for studying intra-population variability and interpopulation differences. The technique has also proven effective in tissue and object segmentation via registration of anatomical labels. However, a major barrier to the use of this approach is its high computational cost. Especially with the increasing number of inputs and data size, it becomes impractical even with a fully optimized implementation on CPUs. Fortunately, the highly element-wise independence of the problem makes it well suited for parallel processing. This paper presents an efficient implementation of unbiased diffeomorphic atlas construction on the new parallel processing architecture based on Multi-Graphics Processing Units (Multi-GPUs). Our results show that the GPU implementation gives a substantial performance gain on the order of twenty to sixty times faster than a single CPU and provides an inexpensive alternative to large distributed-memory CPU clusters.