P1F-8 Identifying Nonlinear Characteristics for the Bulk Response of Ultrasound Contrast Agents

Ultrasound contrast agents consist of gas-filled microbubbles stabilized by a shell. Under ultrasound insonification, these bubbles oscillate nonlinearly with resonance frequencies being well within the diagnostic range. Currently, different detection methods are proposed, often with a heuristic reasoning based on the bubble nonlinearity being modeled by a time-invariant polynomial characteristic. However, it has been demonstrated [Borsboom, 2005] that microbubbles exhibit the behavior of a nonlinearity with memory. To optimize detection schemes, we propose to take this into account by ultrasound contrast agent modeling with a Wiener series. With these models, which can be identified from acoustic measurements, nonlinear system theory can be applied to improve detection methods. The feasibility of contrast agent modeling by Wiener series was evaluated on a contrast agent simulation, implemented by a modified Rayleigh-Plesset differential equation. For a sinusoidal input, the Wiener series approximated contrast agent behavior with a mean square error of 7.6% of the power of the contrast agent signal. The Wiener series approach was subsequently validated in an experimental setup where the nonlinear characteristics of a commercially available contrast agent were identified. The model obtained allowed for a mean square prediction error of 2.6 % of the power of the measured signal for a pseudo-random multilevel sequence. With these experiments, it has been shown that the modeling of the oscillation behavior of ultrasound contrast agents with a Wiener series is feasible