Optimized GPU Implementation of Learning-Based Non-Rigid Multi-Modal Registration

摘要

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 times 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.