TY - GEN
T1 - Probabilistic brain fiber tractography on GPUs
AU - Xu, Mo
AU - Zhang, Xiaorui
AU - Wang, Yu
AU - Ren, Ling
AU - Wen, Ziyu
AU - Xu, Yi
AU - Gong, Gaolang
AU - Xu, Ningyi
AU - Yang, Huazhong
PY - 2012
Y1 - 2012
N2 - Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is an emerging technique that explores the structural connectivity of the human brain. The probabilistic fiber tractography based on DT-MRI data behaves more robustly than deterministic approaches in the presence of fiber crossings, but requires more prohibitive computational time. In this work we present a GPU-based probabilistic framework for brain fiber tractography. The framework includes two main steps: 1) Markov-Chain Monte-Carlo (MCMC) sampling, and 2) probabilistic streamlining fiber tracking. We implement the Metropolis-Hastings sampling for local parameter estimation on GPU. In the probabilistic streamlining fiber tracking, we find that fiber lengths are exponentially distributed, and propose a novel segmenting strategy to improve the load balance. On mid-range GPUs, we achieve performance gains up to 34x and 50x over CPUs for the two steps respectively.
AB - Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is an emerging technique that explores the structural connectivity of the human brain. The probabilistic fiber tractography based on DT-MRI data behaves more robustly than deterministic approaches in the presence of fiber crossings, but requires more prohibitive computational time. In this work we present a GPU-based probabilistic framework for brain fiber tractography. The framework includes two main steps: 1) Markov-Chain Monte-Carlo (MCMC) sampling, and 2) probabilistic streamlining fiber tracking. We implement the Metropolis-Hastings sampling for local parameter estimation on GPU. In the probabilistic streamlining fiber tracking, we find that fiber lengths are exponentially distributed, and propose a novel segmenting strategy to improve the load balance. On mid-range GPUs, we achieve performance gains up to 34x and 50x over CPUs for the two steps respectively.
KW - DT-MRI
KW - GPU
KW - MCMC
KW - Probabilistic Streamlining
KW - Probabilistic fiber tractography
UR - http://www.scopus.com/inward/record.url?scp=84867430877&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867430877&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2012.92
DO - 10.1109/IPDPSW.2012.92
M3 - Conference contribution
AN - SCOPUS:84867430877
SN - 9780769546766
T3 - Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
SP - 742
EP - 751
BT - Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
T2 - 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
Y2 - 21 May 2012 through 25 May 2012
ER -