Machine Learning-Based Double-Profile Intersection for Pointwise Prediction of Shear Elastic Modulus through Support Vector Regression

Double-profile intersection (DoPIo) ultrasound is a new acoustic radiation force (ARF) imaging technique that exploits scatterer shearing under two different tracking point spread functions to estimate shear elastic moduli. Previous versions of DoPIo used an empirically derived model to relate the time where two displacement profiles, each measuring displacement in identical positions using different aperture sizes. We herein propose an alternative, machine learning-based approach that obviates the need for an empirically-derived model by replacing it with a support vector machine that uses paired displacement profiles in their entirety. In this work, we describe the implementation of such a model, validate its ability in shear modulus for simulated materials, and deploy it to image a calibrated phantom.