Search

Quantification of stiffness changes may provide important diagnostic information and aid in the early detection of cancers. Shear wave elastography is an imaging technique that assesses tissue stiffness using acoustic radiation force as an alternate to manual palpation reported previously with quasistatic elastography. In this study, the elastic properties of tissue mimicking materials, including agar, polyacrylamide (PAA), and silicone, are evaluated with an objective to determine material characteristics which resemble normal and cancerous prostate tissue.Acoustic properties and stiffness of tissue mimicking phantoms were measured using compressional mechanical testing and shear wave elastography using supersonic shear imaging. The latter is based on the principles of shear waves generated using acoustic radiation force. The evaluation included tissue mimicking materials (TMMs) within the prostate at different positions and sizes that could mimic cancerous and normal prostate tissue. Patient data on normal and prostate cancer tissues quantified using biopsy histopathology were used to validate the findings. Pathologist reports on histopathology were blinded to mechanical testing and elastographic findings.Young's modulus values of 86.2 ± 4.5 and 271.5 ± 25.7 kPa were obtained for PAA mixed with 2% Al(2)O(3) particles and silicone, respectively. Young's modulus of TMMs from mechanical compression testing showed a clear trend of increasing stiffness with an increasing percentage of agar. The silicone material had higher stiffness values when compared with PAA with Al(2)O(3). The mean Young's modulus value in cancerous tissue was 90.5 ± 4.5 kPa as compared to 93.8 ± 4.4 and 86.2 ± 4.5 kPa obtained with PAA with 2% Al(2)O(3) phantom at a depth of 52.4 and 36.6 mm, respectively.PAA mixed with Al(2)O(3) provides the most suitable tissue mimicking material for prostate cancer tumor material, while agar could form the surrounding background to simulate normal prostate tissue.

Previous publications have reported on the use of one-dimensional cross-correlation analysis with beam-steered echo signals. However, this approach fails to accurately track displacements at larger depths (4.5 cm) due to lower signal-to-noise. In this paper, the authors present the use of adaptive parallelogram shaped two-dimensional processing blocks for deformation tracking.Beam-steered datasets were acquired using a VFX 9L4 linear array transducer operated at a 6 MHz center frequency for steered angles from -15 to 15° in increments of 1°, on both uniformly elastic and single-inclusion tissue-mimicking phantoms. Echo signals were acquired to a depth of 65 mm with the focus set at 40 mm corresponding to the center of phantom. Estimated angular displacements along and perpendicular to the beam direction are used to compute axial and lateral displacement vectors using a least-squares approach. Normal and shear strain tensor component are then estimated based on these displacement vectors.Their results demonstrate that parallelogram shaped two-dimensional deformation tracking significantly improves spatial resolution (factor of 7.79 along the beam direction), signal-to-noise (5 dB improvement), and contrast-to-noise (8-14 dB improvement) associated with strain imaging using beam steering on linear array transducers.Parallelogram shaped two-dimensional deformation tracking is demonstrated in beam-steered radiofrequency data, enabling its use in the estimation of normal and shear strain components.

Electrode vibration elastography is a new shear wave imaging technique that can be used to visualize thermal ablation zones. Prior work has shown the ability of electrode vibration elastography to delineate radiofrequency ablations; however, there has been no previous study of delineation of microwave ablations or radiological-pathological correlations using multiple observers.Radiofrequency and microwave ablations were formed in ex vivo bovine liver tissue. Their visualization was compared on shear wave velocity and maximum displacement images. Ablation dimensions were compared to gross pathology. Elastographic imaging and gross pathology overlap and interobserver variability were quantified using similarity measures.Elastographic imaging correlated with gross pathology. Correlation of area estimates was better in radiofrequency than in microwave ablations, with Pearson coefficients of 0.79 and 0.54 on shear wave velocity images and 0.90 and 0.70 on maximum displacement images for radiofrequency and microwave ablations, respectively. The absolute relative difference in area between elastographic imaging and gross pathology was 18.9% and 22.9% on shear wave velocity images and 16.0% and 23.1% on maximum displacement images for radiofrequency and microwave ablations, respectively.Statistically significant radiological-pathological correlation was observed in this study, but correlation coefficients were lower than other modulus imaging techniques, most notably in microwave ablations. Observers provided similar delineations for most thermal ablations. These results suggest that electrode vibration elastography is capable of imaging thermal ablations, but refinement of the technique may be necessary before it can be used to monitor thermal ablation procedures clinically.

Features extracted from axial-shear strain images of breast masses have been previously utilized to differentiate and classify benign from malignant breast masses. In this paper, we compare shear strain patterns exhibited by both the full-shear (axial and lateral component) versus only the axial-shear strain component for differentiating between bound masses (malignant) when compared to unbound masses (benign).We examine different breast mass characteristics such as mass shape, asymmetric location of masses, stiffness variations, and mass bonding characteristics to background tissue to assess their impact on shear strain patterns generated due to a uniaxial applied deformation. Two-dimensional finite element simulations of both circular and elliptical inclusions embedded within a uniform background were utilized. Different degrees of bonding were characterized using friction coefficient values ranging from 0.01 to 100 denoting loosely bound to firmly bound masses. Single-inclusion tissue-mimicking phantoms mimicking firmly bound and loosely bound ellipsoidal masses oriented at four different angles to the applied deformation were studied to corroborate the mass differentiation performance.Our results indicate that the normalized axial-shear strain and full-shear strain area features are larger for bound when compared to unbound masses. A higher stiffness ratio or contrast between the inclusion and background also improves differentiation. Larger applied deformations reduce the discrimination performance for masses with friction coefficients lower than 0.4, due to increased mass slippage with applied deformations. Potential errors with the use of these features would occur for unbound inclusions at larger applied deformations and for asymmetric mass positions within the background normal tissue.Finite element and tissue-mimicking phantom results demonstrate the feasibility of utilizing both the normalized axial-shear and full-shear strain area features to classify breast masses. Differentiation between bound or unbound masses was not affected by the mass size or shape for masses where the applied deformation is normal to the mass surface. Shear strain patterns vary significantly especially within unbound masses, when the mass surface is not normal to the applied deformation. Discrimination performance for unbound masses was improved by utilizing only the normalized shear strain area patterns located outside the mass as illustrated in this paper.

A three-dimensional finite element analysis based canine heart model is introduced that would enable the assessment of cardiac function.The three-dimensional canine heart model is based on the cardiac deformation and motion model obtained from the Cardiac Mechanics Research Group at UCSD. The canine heart model is incorporated into ultrasound simulation programs previously developed in the laboratory, enabling the generation of simulated ultrasound radiofrequency data to evaluate algorithms for cardiac elastography. The authors utilize a two-dimensional multilevel hybrid method to estimate local displacements and strain from the simulated cardiac radiofrequency data.Tissue displacements and strains estimated along both the axial and lateral directions (with respect to the ultrasound scan plane) are compared to the actual scatterer movement obtained using the canine heart model. Simulation and strain estimation algorithms combined with the three-dimensional canine heart model provide high resolution displacement and strain curves for improved analysis of cardiac function. The use of principal component analysis along parasternal cardiac short axis views is also presented.A 3D cardiac deformation model is proposed for evaluating displacement tracking and strain estimation algorithms for cardiac strain imaging. Validation of the model is shown using ultrasound simulations to generate axial and lateral displacement and strain curves that are similar to the actual axial and lateral displacement and strain curves.

Elastography or elasticity imaging techniques typically image local strains or Young's modulus variations along the insonification direction. Recently, techniques that utilize angular displacement estimates obtained from multiple angular insonification of tissue have been reported. Angular displacement estimates obtained along different angular insonification directions have been utilized for spatial-angular compounding to reduce noise artifacts in axial-strain elastograms, and for estimating the axial and lateral components of the displacement vector and the corresponding strain tensors. However, these angular strain estimation techniques were based on the assumption that noise artifacts in the displacement estimates were independent and identically distributed and that the displacement estimates could be modeled using a zero-mean normal probability density function. Independent and identically distributed random variables refer to a collection of variables that have the same probability distribution and are mutually independent. In this article, a modified least-squares approach is presented that does not make any assumption regarding the noise in the angular displacement estimates and incorporates displacement noise artifacts into the strain estimation process using a cross-correlation matrix of the displacement noise artifacts. Two methods for estimating noise artifacts from the displacement images are described. Improvements in the strain tensor (axial and lateral) estimation performance are illustrated utilizing both simulation data obtained using finite-element analysis and experimental data obtained from a tissue-mimicking phantom. Improvements in the strain estimation performance are quantified in terms of the elastographic signal-to-noise and contrast-to-noise ratios obtained with and without the incorporation of the displacement noise artifacts into the least-squares strain estimator.