Low Variance Estimation of Backscatter Quantitative Ultrasound Parameters Using Dynamic Programming

Abstract

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.

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

“…In our case, the heaviest calculations to be performed by the solver are independent of the data, such that they can be precomputed offline, which makes the method very useful for real-time applications. This single-shot non-iterative estimator is in contrast with other techniques that employ iterative solvers [26], or suffer from high computation cost [28]. Additionally, our method combines both the frequency and depth dimension in a single estimation problem, providing a global estimator for all the depths combined in contrast to the averaging of independent local estimators in other approaches.…”

“…As a benchmark, we consider the dynamic programming (DP) based methodology that has been proposed in [28]. In this work, the authors exploited the depth-continuity of the tissue parameters in a regularized cost function.…”

“…However, this method was proposed only for attenuation estimation. Estimation of both the backscatter and attenuation coefficients with 2 norm depth-regularization in [28] and later on with 1 norm regularization in [29] was performed using dynamic programming (DP) to solve the considered regularized least squares based cost function. While this DP based approach was demonstrated to perform better than the least squares method in [26], it might not be feasible for real-time applications due to the computational lag experienced while evaluating the cost function at each depth for all the possible values of the parameters within the user-specified search range.…”

Estimation of Backscatter Coefficients Using an In Situ Calibration Source

“…This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ by incorporating prior information into a regularization scheme [20]. In an interlaboratory comparison of the BSC estimation approaches, to enable accurate BSC estimates, the transmission coefficient was provided to the participants to account for losses induced by layers on the phantom materials used in the study [23].…”

System-Independent Ultrasound Attenuation Coefficient Estimation Using Spectra Normalization

“…At present, the two commonly used ACE methods in ultrasound array imaging systems are the spectral shift method [13][14][15] and the reference phantom-based methods [10,[16][17][18][19][20][21], with many efforts on improving the methods' stability [22][23][24][25]. The spectral shift method estimates the attenuation coefficient through the downshift of the ultrasound center frequency with increasing depth [21].…”