Spatial Angular Compounding With Affine-Model-Based Optical Flow for Improvement of Motion Estimation

IEEE Trans Ultrason Ferroelectr Freq Control. 2019 Apr;66(4):701-716. doi: 10.1109/TUFFC.2019.2895374. Epub 2019 Jan 25.

Abstract

Tissue motion estimation is an essential step for ultrasound elastography. Our previous study has shown that the affine-model-based optical flow (OF) method outperforms the normalized cross-correlation-based block matching (BM) method in motion estimation. However, the quality of lateral estimation using OF is still low due to inherent limitation of ultrasound imaging. BM-based spatial angular compounding (SAC) has been developed to obtain better motion estimation. In this paper, OF-based SAC (OF-SAC) is proposed to further improve the performance of lateral (and axial) estimation, and it is compared with BM-based SAC (BM-SAC). Plane wave as well as focused wave is transmitted in both simulations and phantom experiments on a linear array. In order to compare the performance quantitatively, the root-mean-square error (RMSE) of axial/lateral displacement and strain, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of axial/lateral strain are used as the evaluation criteria in the simulations. In the phantom experiments, the SNR and CNR are used to assess the quality of axial/lateral strain. The results show that for both OF and BM, SAC improves the performance of motion estimation, regardless of using plane or focused wave transmission. More importantly, OF-SAC is shown to outperform BM-SAC with lower RMSE, higher SNR, and higher CNR. In addition, preliminary in vivo experiments on the carotid artery of a healthy human subject also prove the superiority of OF-SAC. These results suggest that OF-SAC is preferred for both axial and lateral motion estimation to BM-SAC.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Carotid Arteries / diagnostic imaging
  • Computer Simulation
  • Elasticity Imaging Techniques / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Male
  • Movement / physiology
  • Phantoms, Imaging
  • Signal-To-Noise Ratio