title: Fast 2-D ultrasound strain imaging: the benefits of using a GPU.
abstract: 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.
authors.forename: Tim
authors.initials: T
authors.lastname: Idzenga
authors.forename: Evghenii
authors.initials: E
authors.lastname: Gaburov
authors.forename: Willem
authors.initials: W
authors.lastname: Vermin
authors.forename: Jan
authors.initials: J
authors.lastname: Menssen
authors.forename: Chris
authors.initials: C
authors.lastname: de Korte
mesh_terms: D000465:Algorithms; D003196:Computer Graphics / Q000295:instrumentation*; D054459:Elasticity Imaging Techniques / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007089:Image Enhancement / Q000295:instrumentation* / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*