Phantom investigation of phase-inversion-based dual-frequency excitation imaging for improved contrast display

Abstract

Objective The goal of this work is to examine the effects of pulse-inversion (PI) technique in combination with dual-frequency (DF) excitation method to separate the high-order nonlinear responses from microbubble contrast agents for improvement of image contrast. DF excitation method has been previously developed to induce the low-frequency ultrasound nonlinear responses from bubbles by using the composition of two high-frequency sinusoids ( f 1 and f 2). Motivation Although the simple filtering was conventionally utilized to provide signal separation, the PI approach is better in the sense that it minimizes the mutual interferences among these high-order nonlinear responses in the presence of spectral overlap. The novelty of the work is that, in addition to the common PI summation, the PI subtraction was also applied in DF excitation method. Methods DF excitation pulses having an envelope frequency of 3 MHz (i.e., f 1 = 8.5 MHz and f 2 = 11.5 MHz) with pulse lengths of 3–10 μs and the pressure amplitudes from 0.5 to 1.5 MPa were used to interrogate the nonlinear responses of SonoVue™ microbubbles in the phantom experiments. The high-order nonlinear responses in the DF excitation were extracted for contrast imaging using PI summation for even-order nonlinear components or PI subtraction for odd-order nonlinear ones. Results Our results indicated that, as compared to the conventional filtering technique, the PI processing effectively increases the contrast-to-tissue ratio (CTR) of the third-order nonlinear response at 5.5 MHz and the fourth-order nonlinear response at 6 MHz by 2–5 dB. For these high-order nonlinear components, the CTR increase varies with the transmission pressures from 0.5 to 1.5 MPa due to the microbubbles’ displacement induced by the radiation force of DF excitation. Conclusions For DF excitation technique, the PI processing can help to extract either the odd-order or the even-order nonlinear components for higher CTR estimates.