TY - GEN
T1 - Optimized GPU implementation of learning-based non-rigid multi-modal registration
AU - Fan, Zhe
AU - Vetter, Christoph
AU - Guetter, Christoph
AU - Yu, Daphne
AU - Westermann, Rüdiger
AU - Kaufman, Arie
AU - Xu, Chenyang
PY - 2008
Y1 - 2008
N2 - Non-rigid multi-modal volume registration is computationally intensive due to its high-dimensional parameter space, where common CPU computation times are several minutes. Medical imaging applications using registration, however, demand ever faster implementations for several purposes: matching the data acquisition speed, providing smooth user interaction and steering for quality control, and performing population registration involving multiple datasets. Current GPUs offer an opportunity to boost the registration speed through high, computational power at low cost. In our previous work, we have presented a GPU implementation of a non-rigid multi-modal volume registration that was 6-8 limes faster than a software implementation. in this paper, we extend this; work by describing how new features of the DX10-compatible GPUs and additional optimization strategies can be employed to further improve the algorithm performance. We have compared our optimized version with the previous version on the same GPU, and have observed a speedup factor of 3.6. Compared with the software Implementation, we achieve a speedup factor of up to 44.
AB - Non-rigid multi-modal volume registration is computationally intensive due to its high-dimensional parameter space, where common CPU computation times are several minutes. Medical imaging applications using registration, however, demand ever faster implementations for several purposes: matching the data acquisition speed, providing smooth user interaction and steering for quality control, and performing population registration involving multiple datasets. Current GPUs offer an opportunity to boost the registration speed through high, computational power at low cost. In our previous work, we have presented a GPU implementation of a non-rigid multi-modal volume registration that was 6-8 limes faster than a software implementation. in this paper, we extend this; work by describing how new features of the DX10-compatible GPUs and additional optimization strategies can be employed to further improve the algorithm performance. We have compared our optimized version with the previous version on the same GPU, and have observed a speedup factor of 3.6. Compared with the software Implementation, we achieve a speedup factor of up to 44.
KW - GPU
KW - Kullback-Leibler divergence
KW - Learning
KW - Multi-modal volumes
KW - Mutual information
KW - Non-rigid registration
UR - http://www.scopus.com/inward/record.url?scp=43449119835&partnerID=8YFLogxK
U2 - 10.1117/12.770735
DO - 10.1117/12.770735
M3 - Conference contribution
AN - SCOPUS:43449119835
SN - 9780819470980
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2008
T2 - Medical Imaging 2008: Image Processing
Y2 - 17 February 2008 through 19 February 2008
ER -