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Estimation of the local acoustic attenuation from backscattered signals has several clinical applications but remains an open problem. Most of the proposed solutions relate the observed spectral changes directly to the theoretical predictions. However, these methods make a number of approximations and require correction strategies for acoustic wave...
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... However, more studies are required to assess the performance of this method especially in inhomogeneous media. Other approaches such as the iterative reconstruction method proposed by Ilyina et al. [32] aims at simultaneously deriving several parameters including attenuation coefficients by minimizing the difference between measured and simulated power spectra, with the latter being generated using current estimates of ultrasonic parameters. Although the method has promising estimation accuracy (i.e., bias of less than 10%), the assumption of 1-D propagation model may be limited in clinical applications where transducer diffraction effects need to be taken into account. ...
The attenuation coefficient slope (ACS) has the potential to be used for tissue characterization and as a diagnostic ultrasound tool, hence complementing B-mode images. The ACS can be valuable for estimation of other ultrasound parameters such as the backscatter coefficient. There is a well-known trade-off between the precision of the estimated ACS values and the data block size used in spectral-based techniques such as the spectral log difference (SLD). This trade-off limits the practical usefulness of spectral-based attenuation imaging techniques. In this work, the regularized spectral log difference (RSLD) technique is presented in detail and evaluated with simulations and experiments with physical phantoms, ex vivo and in vivo. The RSLD technique allowed decreasing estimation variance when using small data block sizes, i.e., fivefold reduction in the standard deviation of percentage error when using data block sizes larger than 20λ×20λ and more than a tenfold reduction when using 10λ×10λ data blocks. The precision improvement was obtained without sacrificing estimation accuracy (i.e., estimation bias improved in 70% of the cases by 10% of the ground truth value on average while degraded in 30% of the cases by 3% of the ground truth value on average). The improvements in precision allowed for better differentiation of inclusions especially when using small data blocks (i.e., smaller than 20λ×20λ) where the contrast-to-noise ratio improved by an order of magnitude on average. The results suggest the RSLD allows for the reconstruction of attenuation coefficient images with an improved trade-off between spatial resolution and estimation precision.
Estimation of the attenuation is important in medical ultrasound not only for correct time-gain compensation but also for tissue characterization. In this paper, the feasibility of a new method for attenuation estimation is tested. The proposed method estimates the attenuation by repeatedly solving the forward wave propagation problem and matching the simulated signals to the measured ones. This approach allows avoiding common assumptions made by other methodologies and potentially allows to account and correct for other acoustic effects that may bias the attenuation estimate. The performance of the method was validated on simulated data and on data recorded in tissue mimicking phantoms with known attenuation properties, and was compared to the spectral-shift and spectral-difference methods. Simulation results showed the different methods to have good accuracy when noise-free signals were considered (the average relative error of the attenuation estimation did not exceed 15%). However, the accuracy of the conventional methods decreased rapidly in the presence of measurement noise and varying scatterer concentration, while the relative error of the proposed method remained below 15%. Furthermore, the proposed method outperformed conventional attenuation estimators in the experimental phantom study, where its average error was 8%, while the average error of the spectral-shift and spectral-difference methods was 26% and 32%, respectively. In summary, these findings demonstrate the feasibility of the proposed approach and motivate us to refine the method for solving more general problems.
