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    Multi-taper coherence estimates improve predictions of liver scatterer spacing. This advanced ultrasound technique also enhances the ability to distinguish between healthy and ablated liver tissue.

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    Area of Science:

    • Biomedical Ultrasound
    • Medical Imaging
    • Acoustic Signal Processing

    Background:

    • Mean scatterer spacing in liver tissue is a potential indicator of pathological changes.
    • Estimating mean scatterer spacing typically involves analyzing the coherence of ultrasound signals.
    • Current ultrasound methods often use single-taper analysis for coherence estimation.

    Purpose of the Study:

    • To evaluate the efficacy of multi-taper coherence estimates versus single-taper estimates for predicting mean scatterer spacing in liver tissue.
    • To investigate the potential of ultrasound coherence analysis for differentiating between ablated and unablated liver tissue.
    • To assess the performance of a tissue classifier based on multi-taper coherence estimates.

    Main Methods:

    • Frequency-domain Monte Carlo simulations were employed to model ultrasound scattering.
    • Scattering distributions were simulated using Gamma-distributed scatterers with varying fractional standard deviations (5%, 10%, 15%) at a mean scatterer spacing of 1 mm.
    • Coherence estimates were calculated using both single-taper and multi-taper approaches.

    Main Results:

    • Multi-taper estimates of coherence demonstrated superiority over single-taper estimates in predicting mean scatterer spacing.
    • Signal coherence was found to be elevated in unablated liver tissue compared to thermally ablated tissue.
    • A tissue classifier utilizing multi-taper coherence estimates achieved an 8.7% error rate in distinguishing between 23 ablated and 23 unablated regions of interest (ROIs) over a 5 mm gate length.

    Conclusions:

    • Multi-taper spectral analysis provides more accurate predictions of mean scatterer spacing in liver tissue.
    • Ultrasound signal coherence analysis, particularly with multi-taper methods, can effectively differentiate between ablated and unablated liver tissue.
    • The developed multi-taper based tissue classifier shows significant potential for improved diagnostic accuracy in liver tissue assessment.