Microbubble contrast agent detection using binary coded pulses.
Real-time visualization of microbubbles in the microvasculature of deep tissues remains a challenge for existing nonlinear microbubble imaging techniques. A technique with high sensitivity to nonlinear signals is required to compensate for the effects of limited power used to avoid bubble destruction and the high attenuation of the overlying tissues for deeper targets. The use of coded pulses in ultrasound imaging is well established as a means of improving the signal-to-noise ratio (SNR) within B-mode ultrasound imaging, but the feasibility of this approach for detecting microbubbles has not been well studied. In this work we investigate the use of binary phase encoding together with phase and amplitude modulation (PIAM) for the detection of nonlinear signals from microbubbles. A series of simulation experiments were conducted using a modified Rayleigh-Plesset model together with Golay and Barker coding techniques to investigate (i) the ability of binary encoded PIAM to detect nonlinear signals, (ii) the effect of the SNR and insonating pressure on the detection process, (iii) the sensitivity of different pulse encoding approaches and (iv) the effects of bubble resonance behavior on the detection process. The results show that the binary encoding approach combined with PIAM is able to detect nonlinear signals from microbubbles. It was found that nonlinear scattering from the microbubbles degrades the sensitivity of the binary encoded approach such that at high SNR there is no advantage in using these pulses over existing short-pulse PIAM. However, at lower SNR (<20 dB) the increased pulse length provides improved sensitivity without significant loss of spatial resolution, even under conditions in which the detection failed completely for existing approaches. The results also show that both the insonating acoustic pressure and resonance behavior of bubbles have an effect on the detection sensitivity and spatial resolution for the binary encoded approach.
Eckersley
Robert J
RJ
Imaging Sciences Department, Imperial College London, London, UK. r.eckersley@imperial.ac.uk
Tang
Meng-Xing
MX
Chetty
Kevin
K
Hajnal
Joseph V
JV
D001696:Biomechanical Phenomena; D003287:Contrast Media; D005240:Feasibility Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D045423:Microbubbles; D008962:Models, Theoretical; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*
D003287:Contrast Media