GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration
Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
Physics in Medicine and Biology, Vol. 52, No. 19. (21 September 2007), pp. 5771-5783.
@article{sharp2007gpu,
title={GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration},
author={Sharp, GC and Kandasamy, N. and Singh, H. and Folkert, M.},
journal={Physics in medicine and biology},
volume={52},
pages={5771},
year={2007},
publisher={IOP Publishing}
}
This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup—up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.
October 28, 2010 by hgpu
