L1 And L2 Norm Depth-Regularized Estimation Of The Acoustic Attenuation And Backscatter Coefficients Using Dynamic Programming

Abstract

Quantitative Ultrasound (QUS) techniques aim at quantifying backscatter tissue properties to aid in disease diagnosis and treatment monitoring. These techniques rely on accurately compensating for attenuation from intervening tissues. Various methods have been proposed to this end, one of which is based on a Dynamic Programming (DP) approach with a Least Squares (LSq) based cost function and L2 norm regularization to simultaneously estimate attenuation and parameters from the backscatter coefficient. As a way to improve the accuracy and precision of this DP method, we propose to use L1 norm instead of L2 norm as the regularization term in our cost function and optimize the function using DP. Our results show that DP with L1 regularization substantially reduces bias of attenuation and backscatter parameters compared to DP with L2 norm. Furthermore, we employ DP to estimate the QUS parameters of two new phantoms with large scatterer size and compare the results LSq, L2 norm DP and L1 norm DP. Our results show that L1 norm DP outperforms L2 norm DP, which itself outperforms LSq.