Second-Order Ultrasound Elastography with L1-norm Spatial Regularization

TL;DR

This paper introduces L1-SOUL, a novel ultrasound elastography method using L1-norm regularization for improved boundary sharpness and contrast in tissue displacement estimation, outperforming existing algorithms.

Contribution

The paper proposes a new second-order ultrasound elastography algorithm employing L1-norm spatial regularization, addressing blurring and contrast issues in displacement and strain maps.

Findings

L1-SOUL outperforms existing methods in sharpness and contrast.

Achieves up to 117.35% CNR improvement in in vivo data.

Handles non-differentiability of L1-norm via smoothing and iterative optimization.

Abstract

Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a non-linear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2-norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2-, L1-norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1-norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography with L1-norm spatial regularization (L1-SOUL). In terms of both sharpness and visual contrast, L1-SOUL substantially outperforms GLUE, OVERWIND, and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1-SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1-SOUL code can be downloaded from http://code.sonography.ai.