Paralelización de los procesos de conformación de haz para imagen ultrasónica con técnicas GPGPU

  1. D. Romero-Laorden 1
  2. O. Martínez-Graullera 1
  3. C.J. Martín-Arguedas 1
  4. A. Ibañez 1
  5. L.G. Ullate 1
  1. 1 Centro de Acústica Aplicada y Evaluación no Destructiva - CAEND (CSIC-UPM)
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2012

Volume: 9

Issue: 2

Pages: 144-151

Type: Article


More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )


Ultrasonic image generation based on Synthetic Aperture Focusing Techniques (SAFT) can be divided into two stages: (1) the excitation and acquisition stage, where the signals received by each element or group of elements are stored; and (2) the beamforming stage, where the signals are combined together to obtain the image pixels. The use of Graphics Processing Units (GPUs) can significantly reduce the computing time of this last stage, that usually includes di_erent functions such as dynamic focusing, band-pass filtering, spatial filtering or envelope detection. This work studies the parallelization of the beamforming process for ultrasonic imaging and presents its implementation using GPGPU techniques (General Purpose Computation on Graphics Processing Units). We also consider the execution times from the number of signals involved and the desired image dimensions. Experimental results show that using GPU can accelerate, in more than one order of magnitude with respect to CPU implementations, the beamforming and post-processing algorithms making possible the development of real time SAFT imaging systems.

Bibliographic References

  • Corl, P. D., Grant, P. M., Kino, G. S., Septiembre 1978. A digital synthetic focus acoustic imaging system for nde. IEEE Ultrasonic Symposium, 263–268.
  • Goodman, N. A., Stiles, J. M., Julio 2001. The information content of multiple receive aperture sar systems. Proceedings of IEEE Geoscience and Remote Sensing Symposium 4, 1614–1616.
  • Jensen, J. A., Nikolov, S. I., Gammelmark, K. L., Pedersen, M. H., Diciembre 2006. Synthetic aperture ultrasound imaging. Ultrasonics 44, e6–e16.
  • Kino, G. S., Enero 1987. Acoustic Waves: Devices, Imaging, and Analog Signal Processing. Prentice Hall.
  • Lindholm, E., Nickolls, J., Oberman, S., Montrym, J., Marzo 2008. Nvidia tesla: A unified graphics and computing architecture. IEEE Micro 28 (2), 39– 55.
  • Lockwood, G. R., Talman, J. R., Brunke, S. S., Julio 1998. Real-time 3-d ultrasound imaging using sparse synthetic aperture beamforming. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 45, 980–988.
  • Martin, C. J., Martinez, O., Ullate, L. G., Noviembre 2008. Reduction of grating lobes in saft images. IEEE International Ultrasonics Symposium, 721–724.
  • Nickolls, J., Buck, I., Garland, M., Skadron, K., Abril 2008. Scalable parallel programming with cuda. Queue 6 (2), 40–53.
  • Nvidia, C., Marzo 2010. NVIDIA CUDA 3.0 Guía de programación.
  • Smith, S. W., Febrero 1998. Digital Signal Processing. Analog Devices.
  • Steinberg, B. D., Enero 1976. Principles of Aperture and Array System Design. Wiley-Interscience.
  • Yen, N. C., Carey, W., Agosto 1989. Application of synthetic-aperture processing to towed-array data. The Journal of the Acoustical Society of America, 158–171.