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Real-Time Elasticity Imaging

Ultrasound elasticity imaging was introduced in early 1990’s and has been developed for last two decades. Although elasticity imaging can be very promising for many diagnostic purposes, it is not widely used in clinics yet. To actively utilize tissue elasticity information produced by ultrasound elasticity imaging for diagnosing breast cancer, prostate cancer, deep vein thrombosis, and so on, real-time imaging may be necessary because real-time feedback can help physicians to make the best decision.

To cost-effectively implement the real-time elasticity imaging system, optimizations and developments of the processing algorithm for specifically real-time freehand imaging are required. Through hardware and processing sequence optimizations, it was possible to implement the real-time elasticity imaging module (30frames/second) on only one FPGA (Field Programmable Gate Arrays, Xilinx Vertex 4 Series: XC4VSX55). Figure 1 shows the real-time elasticity imaging system developed in collaborating with WinProbe Corporation (North Palm Beach, FL).

Inside the system, a new method to improve elasticity image quality was implemented. In ultrasound elasticity imaging, the tissue is deformed from the surface of transducer and the internal strain is measured by computing the differential displacements between two ultrasound frames. Since the displacement is a continuous function of depth, the sub-pixel displacements are essentially needed. The accuracy in estimation of such displacements dominates the quality of displacement vectors and, therefore, directly affects the quality of elasticity images. An autocorrelation-based method for reducing sub-pixel displacement estimation errors is developed. Figure 2 shows the basic concept of the autocorrelation-based displacement noise reduction method.

Application: Prostate Cancer Detection using Elasticity Imaging

Prostate cancer is one of the leading cancer causes of men deaths. Early detection of prostate cancer is essential to provide definitive treatment and improve patient survival. Figure 3 shows the results of initial clinical studies for the prostate cancer detection.

Clearly, several hard regions of prostate were identified using ultrasound elasticity imaging. Suspicious cancer regions obtained using elasticity imaging were confirmed as cancer through histological analysis.

Our Publications:

S. Kim, S. Park, S.R. Aglyamov, S. Claffey, W.G. Scott, and S.Y. Emelianov, “FPGA-based real-time ultrasound elasticity imaging system,” in Proceedings of the Seventh International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, October 27-30, p.111 (2008) PDF

S. Kim, S. Park, S.R. Aglyamov, M. O’Donnell, and S.Y. Emelianov, “Improvement of displacement estimation using autocorrelation,” in Proceedings of the Seventh International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, October 27-30, p.58 (2008)PDF