Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
A Multilevel Method for Image Registration
SIAM Journal on Scientific Computing
Guest editorial: High-performance computing using accelerators
Parallel Computing
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Fast Deformable Registration on the GPU: A CUDA Implementation of Demons
ICCSA '08 Proceedings of the 2008 International Conference on Computational Sciences and Its Applications
A GPGPU approach for accelerating 2-d/3-d rigid registration of medical images
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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Medical image processing is becoming a significant discipline in bioinformatic. Particularly, deformable registration methods are one of the field most important in the biomedical image processing, due to the valuable information provided. However, these methods consume a considerable processing time and memory requirements. Current GPUs have a high number of cores and high memory bandwidth providing an excellent platform for reducing the cost of these methods in terms of processing time. In this work, it is proposed a Graphics Processing Units (GPU)-based implementation of one of the most sophisticated deformable registration algorithms, DARTEL. The experimental results show a processing time reduction higher than 2 hours in typical cases of study. Moreover, the power consumption is also reduced in a significant amount.