Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images.
Due to processing constraints, automatic image-based registration of medical images has been largely used as a pre-operative tool. We propose a novel method named sort and count for efficient parallelization of mutual information (MI) computation designed for massively multi-processing architectures. Combined with a parallel transformation implementation and an improved optimization algorithm, our method achieves real-time (less than 1s) rigid registration of 3D medical images using a commodity graphics processing unit (GPU). This represents a more than 50-fold improvement over a standard implementation on a CPU. Real-time registration opens new possibilities for development of improved and interactive intraoperative tools that can be used for enhanced visualization and navigation during an intervention.
Shams
Ramtin
R
The Australian National University, Canberra, ACT, Australia. ramtin.shams@anu.edu.au
Sadeghi
Parastoo
P
Kennedy
Rodney
R
Hartley
Richard
R
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003952:Diagnostic Imaging; D021621:Imaging, Three-Dimensional / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods