Nonlinear elasticity imaging: theory and phantom study.
In tissue the Young's modulus cannot be assumed constant over a wide deformation range. For example, direct mechanical measurements on human prostate show up to a threefold increase in Young's modulus over a 10% deformation. In conventional elasticity imaging, these effects produce strain-dependent elastic contrast. Ignoring these effects generally leads to suboptimal contrast (stiffer tissues at lower strain are contrasted against softer tissues at higher strain), but measuring the nonlinear behavior results in enhanced tissue differentiation. To demonstrate the methods extracting nonlinear elastic properties, both simulations and measurements were performed on an agar-gelatin phantom. Multiple frames of phase-sensitive ultrasound data are acquired as the phantom is deformed by 12%. All interframe displacement data are brought back to the geometry of the first frame to form a three-dimensional (3-D) data set (depth, lateral, and preload dimensions). Data are fit to a 3-D second order polynomial model for each pixel that adjusts for deformation irregularities. For the phantom geometry and elastic properties considered in this paper, reconstructed frame-to-frame strain images using this model result in improved contrast to noise ratios (CNR) at all preload levels, without any sacrifice in spatial resolution. From the same model, strain hardening at all preload levels can be extracted. This is an independent contrast mechanism. Its maximum CNR occurs at 5.13% preload, and it is a 54% improvement over the best case (preload 10.6%) CNR for frame-to-frame strain reconstruction. Actual phantom measurements confirm the essential features of the simulation. Results show that modeling of the nonlinear elastic behavior has the potential to both increase detectability in elasticity imaging and provide a new independent mechanism for tissue differentiation.
Erkamp
Ramon Q
RQ
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Emelianov
Stanislav Y
SY
Skovoroda
Andrei R
AR
O'Donnell
Matthew
M
D000465:Algorithms; D016880:Anisotropy; D003238:Connective Tissue / Q000000981:diagnostic imaging* / Q000502:physiology*; D004548:Elasticity; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013314:Stress, Mechanical; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods*