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Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST).

Publication ,  Journal Article
Ahmed, R; Flint, KM; Morgan, MR; Trahey, GE; Walker, WF
Published in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control
July 2022

Multi-covariate imaging of sub-resolution targets (MIST) is a statistical, model-based image formation technique that smooths speckles and reduces clutter. MIST decomposes the measured covariance of the element signals into modeled contributions from mainlobe, sidelobes, and noise. MIST covariance models are derived from the well-known autocorrelation relationship between transmit apodization and backscatter covariance. During in vivo imaging, the effective transmit aperture often deviates from the applied apodization due to nonlinear propagation and wavefront aberration. Previously, the backscatter correlation length provided a first-order measure of these patient-specific effects. In this work, we generalize and extend this approach by developing data-adaptive covariance estimation, parameterization, and model-formation techniques. We performed MIST imaging using these adaptive models and evaluated the performance gains using 152 tissue-harmonic scans of fetal targets acquired from 15 healthy pregnant subjects. Compared to standard MIST imaging, the contrast-to-noise ratio (CNR) is improved by a median of 8.3%, and the speckle signal-to-noise ratio (SNR) is improved by a median of 9.7%. The median CNR and SNR gains over B-mode are improved from 29.4% to 40.4% and 24.7% to 38.3%, respectively. We present a versatile empirical function that can parameterize an arbitrary speckle covariance and estimate the effective coherent aperture size and higher order coherence loss. We studied the performance of the proposed methods as a function of input parameters. The implications of system-independent MIST implementation are discussed.

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Published In

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

DOI

EISSN

1525-8955

ISSN

0885-3010

Publication Date

July 2022

Volume

69

Issue

7

Start / End Page

2303 / 2317

Related Subject Headings

  • Ultrasonography
  • Signal-To-Noise Ratio
  • Pregnancy
  • Phantoms, Imaging
  • Humans
  • Female
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering
  • 09 Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ahmed, R., Flint, K. M., Morgan, M. R., Trahey, G. E., & Walker, W. F. (2022). Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST). IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(7), 2303–2317. https://doi.org/10.1109/tuffc.2022.3178035
Ahmed, Rifat, Katelyn M. Flint, Matthew R. Morgan, Gregg E. Trahey, and William F. Walker. “Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST).IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, no. 7 (July 2022): 2303–17. https://doi.org/10.1109/tuffc.2022.3178035.
Ahmed R, Flint KM, Morgan MR, Trahey GE, Walker WF. Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST). IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2022 Jul;69(7):2303–17.
Ahmed, Rifat, et al. “Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST).IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 7, July 2022, pp. 2303–17. Epmc, doi:10.1109/tuffc.2022.3178035.
Ahmed R, Flint KM, Morgan MR, Trahey GE, Walker WF. Adaptive Models for Multi-Covariate Imaging of Sub-Resolution Targets (MIST). IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2022 Jul;69(7):2303–2317.

Published In

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

DOI

EISSN

1525-8955

ISSN

0885-3010

Publication Date

July 2022

Volume

69

Issue

7

Start / End Page

2303 / 2317

Related Subject Headings

  • Ultrasonography
  • Signal-To-Noise Ratio
  • Pregnancy
  • Phantoms, Imaging
  • Humans
  • Female
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering
  • 09 Engineering