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Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets.

Publication ,  Journal Article
Morgan, MR; Trahey, GE; Walker, WF
Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control
October 2020

Multi-covariate Imaging of Sub-resolution Targets (MIST) is an estimation-based method of imaging the statistics of diffuse scattering targets. MIST estimates the contributions of a set of covariance models to the echo data covariance matrix. Models are defined based on a spatial decomposition of the theoretical transmit intensity distribution into ON-axis and OFF-axis contributions, delineated by a user-specified spatial cutoff. We define this cutoff as the region of interest width (ROI width). In our previous work, we selected the ROI width as the first zero crossing separating the mainlobe from the sidelobe regions. This article explores the effects of varying two key parameters on MIST image quality: 1) ROI width and 2) the degree of spatial averaging of the measured echo data covariance matrix. These results demonstrate a fundamental tradeoff between resolution and speckle texture. We characterize MIST imaging performance across these tunable parameters in a number of simulated, phantom, and in vivo liver applications. We consider performance in noise, fidelity to native contrast, resolution, and speckle texture. MIST is also compared with varying levels of spatial and frequency compounding, demonstrating quantitative improvements in image quality at comparable levels of speckle reduction. In an in vivo example, optimized MIST images demonstrated 20.2% and 13.4% improvements in contrast-to-noise ratio over optimized spatial and frequency compounding images, respectively. These results present a framework for selecting MIST parameters to maximize speckle signal-to-noise ratio without an appreciable loss in resolution.

Duke Scholars

Published In

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

DOI

EISSN

1525-8955

ISSN

0885-3010

Publication Date

October 2020

Volume

67

Issue

10

Start / End Page

1980 / 1992

Related Subject Headings

  • Ultrasonography
  • Signal Processing, Computer-Assisted
  • Male
  • Liver
  • Image Processing, Computer-Assisted
  • Humans
  • Algorithms
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering
 

Citation

APA
Chicago
ICMJE
MLA
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Morgan, M. R., Trahey, G. E., & Walker, W. F. (2020). Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(10), 1980–1992. https://doi.org/10.1109/tuffc.2020.2993241
Morgan, Matthew R., Gregg E. Trahey, and William F. Walker. “Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets.IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 67, no. 10 (October 2020): 1980–92. https://doi.org/10.1109/tuffc.2020.2993241.
Morgan MR, Trahey GE, Walker WF. Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2020 Oct;67(10):1980–92.
Morgan, Matthew R., et al. “Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets.IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 67, no. 10, Oct. 2020, pp. 1980–92. Epmc, doi:10.1109/tuffc.2020.2993241.
Morgan MR, Trahey GE, Walker WF. Intrinsic Tradeoffs in Multi-Covariate Imaging of Sub-Resolution Targets. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2020 Oct;67(10):1980–1992.

Published In

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

DOI

EISSN

1525-8955

ISSN

0885-3010

Publication Date

October 2020

Volume

67

Issue

10

Start / End Page

1980 / 1992

Related Subject Headings

  • Ultrasonography
  • Signal Processing, Computer-Assisted
  • Male
  • Liver
  • Image Processing, Computer-Assisted
  • Humans
  • Algorithms
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering