Nightingale Kathy, Palmeri Mark, Dahl Jeremy, Bradway David, Hsu Stephen, Bouchard Richard, Stephen Rosenzweig Doug DuMont, Rotemberg Veronica, Wang Michael, Zhai Liang, Trahey Gregg

Kathy Nightingale, Mark Palmeri, Jeremy Dahl, David Bradway, Stephen Hsu, Richard Bouchard, Doug DuMont Stephen Rosenzweig, Veronica Rotemberg, Michael Wang, Liang Zhai, Gregg Trahey

Department of Biomedical Engineering, Duke University

キーワード :

1.Elasticity Imaging Methods:　All elasticity imaging methods introduce a mechanical excitation into the body, and monitor the tissue response to that excitation using an imaging method, typically either ultrasound or magnetic resonance imaging ［1］. One of the fundamental challenges with elasticity imaging methods that use external mechanical excitation （static or dynamic） is coupling the excitation into the organ/structure of interest. The use of focused acoustic energy （i.e. acoustic radiation force） eliminates this challenge by delivering a spatially localized mechanical excitation directly within the organ of interest. This phenomenon is the basis for a new generation of elasticity imaging methods that utilize acoustic radiation force to mechanically excite tissue in remote locations and portray the tissue response either through relative differences in displacement amplitude or by quantifying the propagation speed of the radiation force induced shear waves.

2.Acoustic Radiation Force:　Acoustic radiation force is applied to absorbing or reflecting materials in the propagation path of an acoustic wave. This phenomenon is caused by a transfer of momentum from an acoustic wave to the propagation medium. The spatial distribution of the radiation force field is determined by both the transmitted acoustic parameters and the acoustic properties of the tissue. In soft tissues, where the majority of attenuation results from absorption, the following equation can be used to determine radiation force magnitude: F＝2αI/c, where F is the acoustic radiation force （in the form of a body force）, c is the sound speed, α is the absorption coefficient, and I is the temporal average intensity at a given spatial location. The region of tissue to which radiation force is applied through the absorption of acoustic energy is called the Region of Excitation （ROE）. For a given clinical application, displacements on the order of 10 microns are desirable, with increasing intensities and/or pulse durations being associated with larger forces, and thus larger displacements. These sequences can be implemented within current FDA limits （MI＜1.9, Ispta＜720 mW/cm²）.

3.Image Generation: Radiation force based imaging methods have been developed that utilize impulsive （i.e. ＜ 1 ms）, harmonic （pulsed）, and steady state excitations. The work discussed herein utilizes impulsive methods, for which two general imaging approaches have been developed: monitoring the tissue response within the ROE （Acoustic Radiation Force Impulse （ARFI） imaging ［2］）, and monitoring the shear wave propagation outside of the ROE （Shear Wave Elasticity Imaging （SWEI）［3］, Super Sonic Imaging （SSI）［4］, LTTP ［5］）.

4.ARFI Imaging:　ARFI imaging creates images of tissue displacement within the ROE at a given time after radiation force excitation （0.2 - 0.7 ms）. These images provide information about relative differences in tissue stiffness, and have spatial resolution that is comparable to that of B-mode, often with greater contrast, providing matched, adjunctive information to that obtained with B-mode imaging. ARFI imaging is implemented using a diagnostic ultrasound scanner and the same transducer to both generate acoustic radiation force, and monitor the resulting tissue displacement. Tracking and pushing beams are interspersed, and displacements are computed using 1-D correlation based methods on sequentially acquired tracking lines. The displacement data from the different radiation force excitations are synthesized into a single dataset representing the displacement response throughout the tissue volume to all the individual excitations at a given time after excitation. Fig. 1 presents matched B-mode and ARFI prostate coronal images obtained in vivo with a transrectal 3D mechanical wobbler curvilinear transducer array （EV9F4） and a modified Siemens Antares^TM scanner, under an IRB approved protocol. In ARFI images, darker regions represent smaller displacements （i.e., stiffer tissues）, and brighter regions represent larger displacements （i.e., softer regions）. The contrast in ARFI images is related to the underlying tissue mechanical contrast, in addition to the relative size and position of the excitation ROE and the structures being imaged. When improved contrast is desired in structures that span an appreciable depth, multiple pushing excitations with different focal depths can be utilized. ARFI imaging has been demonstrated in many clinical settings, including: breast, liver, thyroid, prostate, cardiovascular applications, and real-time monitoring of thermal ablation procedures and guidance of local anesthetic delivery, several of which will be reviewed in this presentation.

5.Quantitative Radiation Force Imaging Methods:　Sarvazyan et. al. ［3］ proposed the quantification of tissue shear modulus via monitoring the propagation speed of the shear waves generated by focused acoustic radiation force. Most approaches that have subsequently been developed utilize time-of-flight methods to evaluate the propagation speed at laterally offset locations from the ROE. Typical shear wave speeds range from 1-10 m/s in the majority of soft tissues; thus, track beam pulse repetition frequencies （PRFs） in the kHz range are required to monitor the propagation. For ultrasonic imaging systems with limited parallel receive capabilities, repeatedly interrogating a given ROE, translating the tracking beam locations after each excitation, and then synthesizing the data can achieve this. Or, systems with extensive parallelism can be utilized, thus reducing the overall energy deposition as is done with SSI. Once propagation data are obtained, the time that the wave arrives at laterally offset positions is determined, and the inverse slope of a linear regression of position vs. arrival time is used to determine the shear wave speed （c_t）, which is related to the shear modulus and density by c_t＝sqrt（μ/ρ）. Super Sonic Imaging （SSI） applies these concepts to generate quantitative images of tissue mechanical properties, including estimation of tissue shear wave speed and dispersion, with promising initial results in breast imaging ［4］. Similar methods for the characterization of liver stiffness have been developed ［5］, and are being used to evaluate the correlation between liver stiffness with biopsy determined fibrosis stage. In this presentation, the above imaging methods will be reviewed, and several clinical imaging examples will be presented.
References
　［1］J. Greenleaf, M. Fatemi, and M. Insana. Annu. Rev. Biomed. Eng., 5（1）:57-78, 2003.
　［2］K.R. Nightingale, M.S. Soo, R.W. Nightingale, and G.E. Trahey. Ultrasound Med. Biol., 28（2）:227-235, 2002.
　［3］A. Sarvazyan, O. Rudenko, S. Swanson, J. Fowlkes, and S. Emelianov.. Ultrasound Med. Biol., 24（9）:1419-1435, 1998.
　［4］M. Tanter, J. Bercoff, A. Athansiou, T. Deffieux, J. Gennison, G. Montaldo, A. Muller, A. Tardivon, and M. Fink. Ultrasound Med. Biol., 34（9）:1373-1386, 2008.
　［5］M.L. Palmeri, M.H. Wang, J.J. Dahl, K.D. Frinkley, and K.R. Nightingale, Ultrasound Med. Biol., 34（4）:546-558, 2008. This work was supported by NIH grants EB-002132, and C A-114075.

Fig. 1: In vivo prostate B-mode and ARFI coronal images with corresponding histologic section obtained post-excision; with stiff region （red arrow） corresponding to cancer （blue）