This paper presents a spectral autoregressive method dedicated to the detection of ultrasound contrast agents (USCA) from radiofrequency (rf) data. The method is based on second-order autoregressive (AR) modeling of the rf signal. Contrast agents induce a second harmonic, which may be efficiently detected through the AR spectrum using the magnitude of the second AR spectral peak (SM2). In contrast to multipulse methods that process two or more rf frames, our method processes a single rf frame. The method is tested by numerical simulation and on in vitro data for contrast agent concentrations ranging from 103 to 50 × 103 bubbles/ml (2 × 10-6 to 10-4 volumic concentration) and mechanical index (MI) ranging from 0.1 to 0.36. The results show that the proposed parameter SM2 enables one to detect correctly the contrast agent, in particular at low concentration and MI (the minimum difference in SM2 between tissue and USCA is 10 dB). Furthermore, the in-vitro data demonstrates that an adapted smoothing technique reduces the variability of SM2 and provides accurate and stable segmentation of the contrast agent perfusion region.
BaldeweckTLaugierPHermentABergerG.Application of autoregressive spectral analysis for ultrasound attenuation estimation: interest in highly attenuating medium, IEEE Trans Ultrason Ferroelectr Freq Control42, 99–110 (1995).
2.
WearKAWagnerRFGarraBS. A comparison of autoregressive spectral estimation algorithms and order determination methods in ultrasonic tissue characterization, IEEE Trans Ultrason Ferroelectr Freq Control42, 709–716 (1995).
3.
GiovannelliJFDemomentGHermentA.A Bayesian method for long AR spectral estimation: a comparative study, IEEE Trans Ultrason Ferroelectr Freq Control43, 220–232 (1996).
4.
GorceJ-MFribouletDDydenkoIProcessing radio frequency ultrasound images: a robust method for local spectral features estimation by a spatially constrained parametric approach, IEEE Trans Ultrason Ferroelectr Freq Control49, 1704–1719 (2002).
5.
BouakazADe JongNCachardC.Standard properties of ultrasound contrast agents, Ultrasound Med Biol24, 469–72 (1998).
6.
FrinkingPJABouakazAKirkhornJCateFTDe JongN.Ultrasound contrast imaging: current and new potential methods, Ultrasound Med Biol26, 965–975 (2000).
7.
HoggJC. Neutrophil kinetics and lung injury, Physiol Rev67, 1249–1295 (1987).
8.
DengCXLizziF.A review of physical phenomena associated with ultrasonic contrast agents and illustrative clinical applications, Ultrasound Med Biol28, 277–286 (2002).
9.
PorterTRFengX.Contrast echocardiography: how will it be used?, ACC Curr J Rev10, 43–47 (2001).
10.
DaleckiDChildSZRaemanCHBioeffects of positive and negative acoustic pressures in mice infused with microbubbles, Ultrasound Med Biol26, 1327–1332 (2000).
11.
MorganKEAverkiouMFerraraKW. The effect of the phase of transmission on contrast agent echoes, IEEE Trans Ultrason Ferroelectr Freq Control45, 872–875 (1998).
12.
SimpsonDChinCBurnsP.Pulse Inversion Doppler: a new method for detecting nonlinear echoes from microbubble contrast agents, IEEE Trans Ultrason Ferroelectr Freq Control46, 372–382 (1999).
13.
WilkeningWGBrendelBJiangHLazenbyJErmentH.Optimized receive filters and phase-coded pulse sequences for contrast agent and nonlinear imaging, in Proc IEEE Ultrason Symp, pp. 1733–1737 (2001).
14.
MorganKEAllenJSFerraraKW. Wideband sub-forcing harmonic phase inversion imaging, in Proc IEEE Ultrason Symp, pp. 1889–1892 (2000).
15.
De JongNBouakazACateFT. Contrast harmonic imaging, Ultrasonics40, 567–573 (2002).
16.
BouakazACateFTDe JongN.A new ultrasonic transducer for improved contrast nonlinear imaging, Phys Med Biol, 49, 3515–3525 (2004).
17.
ForsbergFShiWTGoldbergBB. Subharmonic imaging of contrast agents, Ultrasonics38, 93–98 (2000).
18.
ArditiMBrenierTSchneiderM.Preliminary study in differential contrast echography, Ultrasound Med Biol23, 1185–94 (1997).
19.
BorsboomJChinCTDe JongN.Experimental validation of a nonlinear coded excitation method for contrast agent, in Proc IEEE Ultrason Symp, pp. 1729–1732 (2002).
20.
WilkeningWGLazenbyJErmertH.A method for detecting echoes from mircobubble contrast agents based on time-variance, in Proc IEEE Ultrason Symp, pp. 1823–1826 (1998).
21.
KaySM. Modern Spectral Estimation: Theory and Application (Prentice Hall, Englewood Cliffs, NJ, 1988).
22.
BerthomierCHermentAGiovannelliJFMultigate Doppler signal analysis using 3-D regularized long AR modelling, Ultrasound Med Biol27, 1515–1523 (2001).
23.
ProakisJGMankolakisDG. Digital Signal Processing: Principles, Algorithms and Applications (Prentice Hall International, Upper Sadle River, New Jersey, 1996).
24.
BlakeAZissermanA.Visual Reconstruction (The MIT Press, Cambridge, MA, 1987).
25.
GemanDReynoldsG.Constrained restoration and the recovery of discontinuities, IEEE Trans Pattern Anal Mach Intell14, 367–383 (1992).
26.
ScabiaMBiagiEMasottiL.Hardware and software platform for real-time processing and visualization of echographic radiofrequency signals, IEEE Trans Ultrason Ferroelectr Freq Contr49, 1444–1452 (2002).
27.
GorceJMArditiMSchneiderM.Influence of bubble size distribution on the echogenicity of ultrasound contrast agents: a study of SonoVue, Invest Radiol35, 661–71 (2000).
28.
JensenJA. Field: A program for simulating ultrasound system, Med Biol Eng34, 351–353 (1996).
29.
MorganKEAllenJSDaytonPAExperimental and theoretical evaluation of microbubble behavior: effect of transmitted phase and bubble size, IEEE Trans Ultrason Ferroelectr Freq Control47, 1494–1509 (2000).
30.
DurningBLavalJRogninNCachardC.Ultrasound signals and images simulation of phantoms with contrast agent, in Proc IEEE Ultrason Symp, pp. 1710–1713 (2004).
