In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, "feature-motion decorrelation" still remains as a challenge for the speckle tracking methods. Recently, a coupled filtering method and an affine warping method were proposed to accurately estimate strain values, when the tissue deformation is large. The major drawback of these methods is the high computational complexity. Even the graphics processing unit (GPU)-based program requires a long time to finish the analysis. In this paper, we propose field-programmable gate array (FPGA)-based implementations of both methods for further acceleration. The capability of FPGAs on handling different image processing components in these methods is discussed. A fast and memory-saving image warping approach is proposed. The algorithms are reformulated to build a highly efficient pipeline on FPGA. The final implementations on a Xilinx Virtex-7 FPGA are at least 13 times faster than the GPU implementation on the NVIDIA graphic card (GeForce GTX 580).
Speckle tracking methods refer to motion tracking methods based on speckle patterns in ultrasound images. They are commonly used in ultrasound based elasticity imaging techniques to reveal mechanical properties of tissues for clinical diagnosis. In speckle tracking, feature motion decorrelation exists when speckle patterns are not identical before and after tissue motion and deformation. Feature motion decorrelation violates the underlying assumption of most speckle tracking methods. Consequently, the estimation accuracy of current methods is greatly limited. In this paper, two types of speckle pattern variations, the geometric transformation and the intensity change of speckle patterns, are studied. We show that a coupled filtering method is able to compensate for both types of variations. It provides accurate strain estimations even when tissue deformation or rotation is extremely large. We also show that in most cases, an affine warping method that only compensates for the geometric transformation is able to achieve a similar performance as the coupled filtering method. Feature motion decorrelation in B-mode images is also studied. Finally, we show that in typical elastography studies, speckle tracking methods without modeling local shearing or rotation will fail when tissue deformation is large.
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