Accelerating Multi-level Deformable Image Registration Methods for Lung Images with GPU Computing
Nathan Ellingwood, Youbing Yin, Matthew R. Smith, Ching‐Long Lin · 2010
Results and Conclusions Methods In this work we introduce a novel Diffeomorphic Multi-level Transform Composite method (DMTC) for accelerated computation of nonrigid masspreserving registration of lung images with large deformation on Graphics Processing Units (GPUs). The proposed method dramatically reduces computational time when compared to its single- and multi-threaded CPU counterparts, with the speedup factor ranging from 6 to 112. The significance of this work is two-fold. First, the DMTC achieves computation and memory efficiencies on GPUs, and together with the mass-preserving measure improves the accuracy of registration. Second, the improved computational efficiency is essential in analyzing data for population-based studies and translational science. This GPU implementation can be easily adapted for use with other non-mass-preserving similarity measures.