Passive localization and classification of cavitation activity using group sparsity

Abstract

In therapeutic high intensity focused ultrasound (HIFU) applications, cavitation mapping is a powerful tool to monitor and guide the treatment procedure. Furthermore, the frequency spectrum of the cavitation activity can be used to classify the mode of cavitation (stable/inertial), enabling a means for increasing the safety of the application. In this study, we formulate the cavitation mapping as a group sparse constrained optimization problem, minimizing l2,1-norm of the solution. The frequency bins related to a class of cavitation activity (harmonic, ultra-harmonic, or broadband) are grouped using l2-norm for each voxel, and l1-norm of the image is minimized. We solve this problem using an Augmented Lagrangian Method, specifically the Alternating Direction Method of Multipliers (ADMM). We used a simulation model to test this method on a 300 mm diameter 128-element hemispherical receiver array application. We calculate the radiated pressure from the microbubbles inside the HIFU beam using a rigid vessel bubble dynamics model. Then, we reconstruct the image associated with the bubble activity at the focal region using the received signals for various focal pressure distribution scenarios. The results show that the proposed method provide improved resolution and sensitivity, especially for localizing inertial cavitation activity.