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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.