Acoustic Radiation Force Based Imaging: An Overview
K Nightingale*, M Wang, S Rosenzweig, V Rotemberg, N Rouze, M Palmeri, Duke University, Durham, North CarolinaTH-C-217BCD-6 Thursday 10:30:00 AM - 12:30:00 PM Room: 217BCD
Acoustic radiation force impulse (ARFI) based elasticity imaging methods have been under investigation in research laboratories for over 15 years. In ARFI imaging methods, temporally impulsive (i.e. < 1 msec) focused acoustic radiation force is used to mechanically excite tissue, and the dynamic displacement response is monitored using the same transducer and ultrasonic correlation based techniques. High resolution qualitative images can be generated that portray relative differences in tissue stiffness, maintaining structural integrity in heterogeneous media. These images are generated by monitoring the tissue response along the central axis of the excitation, and sequentially interrogating a 2D (or 3D) field of view (i.e. on-axis, or ARFI imaging). Shear waves are generated by each of the ARFI excitations, and their propagation speed is related to the underlying tissue stiffness. Time-of-flight based algorithms have been developed to estimate shear wave propagation speed, and, under certain simplifying assumptions, the tissue stiffness can be quantified based upon these speeds. In the past 3 years, both Siemens Medical Systems and Supersonic Imagine (SSI) have released commercial ARFI based elasticity imaging products. This talk will provide an overview of the underlying physics of radiation force based elasticity imaging techniques, and a review of the initial clinical reports using these methods.
1) to understand the differences between acoustic images, qualitative elasticity images, and shear wave images
2) to understand the tradeoffs between resolution and accuracy in shear wave imaging
3) to understand the limitations of the assumptions made by time-of-flight based algorithms
Disclosures: Some authors hold patents related to radiation force based techniques, and our lab has a research agreement with Siemens Medical Solutions.
Funding: NIH R01 EB002132; R01 CA142824