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Related Experiment Video

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Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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Accelerating 3-D GPU-based Motion Tracking for Ultrasound Strain Elastography Using Sum-Tables: Analysis and Initial

Bo Peng1, Shasha Luo1, Zhengqiu Xu1

  • 1School of Computer Science, Southwest Petroleum University, Chengdu 610500, China.

Applied Sciences (Basel, Switzerland)
|August 3, 2019
PubMed
Summary

This study explores accelerating 3-D ultrasound strain elastography (USE) motion tracking using graphics processing units (GPUs) and Sum-Table methods. The Luo-Konofagou method significantly improves computational efficiency on GPUs for 3-D USE applications.

Keywords:
block-matchingcorrelationgraphics processing unitmotion trackingsum-tableultrasound elastography

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Science

Background:

  • 3-D ultrasound strain elastography (USE) systems require computationally intensive 3-D motion tracking.
  • Existing Sum-Table methods accelerate signal correlation computation in serial environments.
  • Accelerating 3-D motion tracking is crucial for enhancing 3-D USE system efficiency.

Purpose of the Study:

  • To investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) methods for GPU-accelerated 3-D USE.
  • To compare the computational efficiency and displacement tracking accuracy of two ST-NCC implementations (Lewis et al. and Luo-Konofagou) against the conventional NCC method on a GPU platform.

Main Methods:

  • Implemented three methods—conventional NCC, Lewis ST-NCC, and Luo-Konofagou ST-NCC—using CUDA on a GeForce GTX TITAN X GPU.
  • Evaluated displacement tracking accuracy and computational efficiency using 3-D ultrasound data from a tissue-mimicking phantom experiment.

Main Results:

  • The Luo-Konofagou ST-NCC method improved computational efficiency by 17-46% compared to the classic NCC method on the GPU.
  • The Lewis ST-NCC method showed limited or lower efficiency improvements (7-23%) on the GPU.
  • Both ST-NCC methods achieved comparable displacement tracking accuracy to the conventional NCC method.

Conclusions:

  • The Luo-Konofagou ST-NCC method is a feasible and efficient approach for GPU-accelerated 3-D USE motion tracking.
  • The Lewis ST-NCC method offers less computational advantage in this GPU parallel computing environment.
  • Further optimization of ST-NCC methods could enhance 3-D USE system performance.