A Weighted Hankel Approach and Cram\'er-Rao Bound Analysis for Quantitative Acoustic Microscopy Imaging

TL;DR

This paper presents a novel weighted Hankel spectral method with reweighting for improved noise robustness in quantitative acoustic microscopy, along with Cramér-Rao bounds to benchmark estimation accuracy, validated by simulations and experiments.

Contribution

It introduces a new spectral approach with reweighting for QAM and derives the first Cramér-Rao bounds for acoustic parameter estimation in this context.

Findings

The proposed method outperforms standard autoregressive approaches.

It demonstrates robustness under challenging noise conditions.

Experimental results confirm improved accuracy in tissue characterization.

Abstract

Quantitative acoustic microscopy (QAM) is a cutting-edge imaging modality that leverages very high-frequency ultrasound to characterize the acoustic and mechanical properties of biological tissues at microscopic resolutions. Radio-frequency echo signals are digitized and processed to yield two-dimensional maps. This paper introduces a weighted Hankel-based spectral method with a reweighting strategy to enhance robustness with regard to noise and reduce unreliable acoustic parameter estimates. Additionally, we derive, for the first time in QAM, Cram\'er-Rao bounds to establish theoretical performance benchmarks for acoustic parameter estimation. Simulations and experimental results demonstrate that the proposed method consistently outperform standard autoregressive approach, particularly under challenging conditions. These advancements promise to improve the accuracy and reliability of tissue characterization, enhancing the potential of QAM for biomedical applications.