Abstract

A critical step in quasi-static ultrasound elastography is estimation of time-delay between two frames of Radio-Frequency (RF) data that are obtained while tissue is undergoing deformation. This thesis presents a novel technique for Time-Delay Estimation (TDE) of all samples of RF data simultaneously. A nonlinear cost function that incorporates similarity of RF data intensity and prior information of displacement continuity is formulated. Optimization of this nonlinear function involves searching for TDE of all samples of RF data, rendering the optimization intractable with conventional techniques given that the number of variables can be approximately one million. Therefore, the optimization problem is converted to a sparse linear system of equations, and is solved in real-time using a computationally efficient optimization technique. We call our method GLUE (GLobal Ultrasound Elastography), and compare it to Dynamic Programming Analytic Minimization (DPAM) (Rivaz, Boctor, Choti, & Hager, 2011) and Normalized Cross Correlation (NCC) techniques. We test our method on simulation, phantom, and in-vivo data. The results show that the proposed method outperforms both DPAM and NCC techniques. In another proposed method, we assume tissue deformation can be efficiently approximated by an affine transformation, and hence call our method ATME (Affine Transformation Model Elastography). The affine transformation model is utilized to obtain initial estimates of axial and lateral displacement fields. The nonlinear cost function of GLUE method is used to fine-tune the initial affine deformation field. Results on simulation and RF data we collect from in-vivo patellar tendon and medial collateral ligament (MCL), show that ATME can be used to accurately track tissue displacement.