RAFT-USENet: A Unified Network for Accurate Axial and Lateral Motion Estimation in Ultrasound Elastography Imaging

Abstract

High-quality motion estimation is essential in ultrasound elastography (USE) for evaluating tissue mechanical properties and detecting abnormalities. Traditional methods, such as speckle tracking and regularized optimization, face challenges including noise, oversmoothing of displacements and prolonged runtimes. Recent efforts have explored optical flow-based convolutional neural networks (CNNs). However, current approaches face, at least, one of the following limitations: 1) reliance on tissue incompressibility assumption, which compromises data fidelity and can introduce large errors; 2) dependence on ground truth displacement data for supervised CNN methods; 3) use of a regularizer not aligned with tissue physics by relying on first-order displacement derivatives only; 4) use of a L2-norm regularizer that oversmoothes motion estimates; and 5) substantially large sampling size, increasing computational and memory demands, especially for classical methods. In this paper, we develop RAFT-USENet, a physics-informed, unsupervised neural network to estimate both axial and lateral displacements. We design RAFT-USENet by substantially modifying the optical flow RAFT network to adapt it to high-frequency USE data. Extensive validation using simulation, phantom and in vivo data demonstrates that RAFT-USENet significantly outperforms existing methods, with normalized cross-correlation values of 0.94, 0.88, and 0.82 in simulation, breast phantom and in vivo datasets, respectively, compared to 0.79–0.88, 0.76-0.85, and 0.69-0.81 for the competing methods. Additionally, RAFT-USENet reduces computational time by 1.5-150 times compared to existing methods. These results suggest that RAFT-USENet may be a potentially reliable and accurate tool for clinical elasticity imaging applications.