Reconstructive ultrasound elasticity imaging for renal transplant diagnosis: kidney ex vivo results.

It may be possible to diagnose and monitor scarring, inflammation and edema in transplant kidney using reconstructive ultrasound elasticity imaging. Kidney elasticity is expected to change dramatically with scar, and to a lesser degree, with acute inflammation and edema. The hypothesis that changes in kidney elasticity can be imaged using a clinical ultrasound scanner was experimentally tested with an ex vivo canine kidney model, and results on a single pair of kidneys are reported in this paper. A cross-linking agent affected kidney elasticity both globally and locally. Elasticity changes were monitored with accurate estimates of internal displacement and strain followed by Young's modulus reconstruction. The results of this study strongly suggest that ultrasound elasticity imaging can detect elasticity changes in complex structures such as the kidney. Moreover, it has the potential to become an important clinical tool for renal transplant diagnosis.

Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation.

In ultrasound elasticity imaging, strain decorrelation is a major source of error in displacements estimated using correlation techniques. This error can be significantly decreased by reducing the correlation kernel. Additional gains in signal-to-noise ratio (SNR) are possible by filtering the correlation functions prior to displacement estimation. Tradeoffs between spatial resolution and estimate variance are discussed, and estimation in elasticity imaging is compared to traditional time-delay estimation. Simulations and experiments on gel-based phantoms are presented. The results demonstrate that high resolution, high SNR strain estimates can be computed using small correlation kernels (on the order of the autocorrelation width of the ultrasound signal) and correlation filtering.

Adaptive strain estimation using retrospective processing [medical US elasticity imaging.

Because errors in displacement and strain estimates depend on the magnitude of the induced strain, the strain signal-to-noise ratio (SNR) will be a function of the applied deformation. If deformation is applied at the body surface, it is difficult during data acquisition to select a single surface displacement providing the highest strain SNR throughout the image. By applying continuous deformation and capturing data in real-time, the surface displacement providing the highest strain SNR can be selected retrospectively. A method to adaptively optimize strain SNR over the image plane using retrospective processing is presented and demonstrated with experimental results.

Nonlinear estimation of the lateral displacement using tissue incompressibility.

Using the incompressibility property of soft tissue, high quality lateral displacement distributions can be reconstructed from accurate axial displacement measurements and noisy lateral displacement estimates. Previous methods appropriate for small deformations have been extended for high magnitude deformations requiring a nonlinear model. Problems arising in incompressibility processing for large deformations are considered. Applications of nonlinear incompressibility methods to ultrasonic measurements on gel-based, tissue equivalent phantoms are given. Lateral displacement images reconstructed with nonlinear methods are compared to those reconstructed with linear methods for both small and large deformations.

Elasticity imaging for early detection of renal pathology.

Early detection of renal pathology may be possible with elasticity imaging. This hypothesis was experimentally tested by quantitatively imaging internal mechanical strain due to surface deformations in an in vitro animal model of nephritis. Preliminary data support the hypothesis that kidney elasticity changes with renal damage and concomitant scarring before problems are detectable by traditional diagnostic techniques such as laboratory measurements of renal function.