Fast linear least-squares method for ultrasound attenuation and backscatter estimation

Reverberation clutter signal suppression in ultrasound attenuation estimation using wavelet-based robust principal component analysis

Objective. Ultrasound attenuation coefficient estimation (ACE) has diagnostic potential for clinical applications such as quantifying fat content in the liver. Previously, we have proposed a system-independent ACE technique based on spectral normalization of different frequencies, called the reference frequency method (RFM). This technique does not require a well-calibrated reference phantom for normalization. However, this method may be vulnerable to severe reverberation clutter introduced by the body wall. The clutter superimposed on liver echoes may bias the estimation. Approach. We proposed to use robust principal component analysis, combined with wavelet-based sparsity promotion, to suppress the severe reverberation clutters. The capability to mitigate the reverberation clutters was validated through phantom and in vivo studies. Main Results. In the phantom studies with added reverberation clutters, higher normalized cross-correlation and smaller mean absolute errors were attained as compared to RFM results without the proposed method, demonstrating the capability to reconstruct tissue signals from reverberations. In a pilot patient study, the correlation between ACE and proton density fat fraction (PDFF), a measurement of liver fat by MRI as a reference standard, was investigated. The proposed method showed an improvement of the correlation (coefficient of determination, R = 0.82) as compared with the counterpart without the proposed method (R = 0.69). Significance: The proposed method showed the feasibility of suppressing the reverberation clutters, providing an important basis for the development of a robust ACE with large reverberation clutters.

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.