11364

Fast 2-D Ultrasound Strain Imaging: The Benefits of Using a GPU

Tim Idzenga, Evghenii Gaburov, Willem Vermin, Jan Menssen, Chris L. de Korte
Medical UltraSound Imaging Centre, Department of Radiology, Radboud University
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 61, no. 1, 2014

@article{idzenga2014fast,

   title={Fast 2-D ultrasound strain imaging: the benefits of using a GPU},

   author={Idzenga, Tim and Gaburov, Evghenii and Vermin, Willem and Menssen, J and De Korte, C},

   journal={Ultrasonics, Ferroelectrics and Frequency Control, IEEE Transactions on},

   volume={61},

   number={1},

   pages={207–213},

   year={2014},

   publisher={IEEE}

}

Deformation of tissue can be accurately estimated from radio-frequency ultrasound data using a 2-dimensional normalized cross correlation (NCC)-based algorithm. This procedure, however, is very computationally time-consuming. A major time reduction can be achieved by parallelizing the numerous computations of NCC. In this paper, two approaches for parallelization have been investigated: the OpenMP interface on a multi-CPU system and Compute Unified Device Architecture (CUDA) on a graphics processing unit (GPU). The performance of the OpenMP and GPU approaches were compared with a conventional Matlab implementation of NCC. The OpenMP approach with 8 threads achieved a maximum speed-up factor of 132 on the computing of NCC, whereas the GPU approach on an Nvidia Tesla K20 achieved a maximum speed-up factor of 376. Neither parallelization approach resulted in a significant loss in image quality of the elastograms. Parallelization of the NCC computations using the GPU, therefore, significantly reduces the computation time and increases the frame rate for motion estimation.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: