Modality-independent elastography (MIE) is a method of elastography that reconstructs the elastic properties of tissue
using images acquired under different loading conditions and a biomechanical model. Boundary conditions are a critical
input to the algorithm, and are often determined by time-consuming point correspondence methods requiring manual
user input. Unfortunately, generation of accurate boundary conditions for the biomechanical model is often difficult due
to the challenge of accurately matching points between the source and target surfaces and consequently necessitates the
use of large numbers of fiducial markers. This study presents a novel method of automatically generating boundary
conditions by non-rigidly registering two image sets with a Demons diffusion-based registration algorithm. The use of
this method was successfully performed in silico using magnetic resonance and X-ray computed tomography image data
with known boundary conditions. These preliminary results have produced boundary conditions with accuracy of up to
80% compared to the known conditions. Finally, these boundary conditions were utilized within a 3D MIE
reconstruction to determine an elasticity contrast ratio between tumor and normal tissue. Preliminary results show a
reasonable characterization of the material properties on this first attempt and a significant improvement in the
automation level and viability of the method.
This work explores an inverse problem technique of extracting soft tissue elasticity information via nonrigid model-based
image registration. The algorithm uses the elastic properties of the tissue in a biomechanical model to achieve
maximal similarity between image data acquired under different states of loading. A framework capable of handling
fully three-dimensional models and image data has been recently developed utilizing parallel computing and iterative
sparse matrix solvers. For this preliminary investigation, a series of simulation experiments with clinical image data of
human breast are used to test the robustness of the algorithm to expected mis-estimation of displacement boundary
conditions encountered in real-world situations. Three methods of automated point correspondence are also examined as
means of generating boundary conditions for the algorithm.
Multiple skin conditions exist which involve clinically significant changes in elastic properties.
Early detection of such changes may prove critical in formulating a proper treatment plan. However,
most diagnoses still rely primarily on visual inspection followed by biopsy for histological analysis. As a
result, there would be considerable clinical benefit if a noninvasive technology to study the skin were
available. The primary hypothesis of this work is that skin elasticity may serve as an important method
for assisting diagnosis and treatment. Perhaps the most apparent application would be for the
differentiation of skin cancers, which are a growing health concern in the United States as total annual
cases are now being reported in the millions by the American Cancer Society. In this paper, we use our
novel modality independent elastography (MIE) method to perform dermoscopic skin elasticity
evaluation. The framework involves applying a lateral stretching to the skin in which dermoscopic
images are acquired before and after mechanical excitation. Once collected, an iterative elastographic
reconstruction method is used to generate images of tissue elastic properties and is based on a twodimensional
(2-D) membrane model framework. Simulation studies are performed that show the effects
of three-dimensional data, varying subdermal tissue thickness, and nonlinear large deformations on the
framework. In addition, a preliminary in vivo reconstruction is demonstrated. The results are
encouraging and indicate good localization with satisfactory degrees of elastic contrast resolution.
Recent advances in breast cancer imaging have generated new ways to characterize the disease. Many analysis
techniques require a method for determining correspondence between a pendant breast surface before and after a
deformation. In this paper, an automated point correspondence method that uses the surface Laplacian or the diffusion
equation coupled to an isocontour matching and interpolation scheme are presented. This method is compared to a TPS
interpolation of surface displacements tracked by fiducial markers. The correspondence methods are tested on two
realistic finite element simulations of a breast deformation and on a breast phantom. The Laplace correspondence
method resulted in a mean TRE ranging from 1.0 to 7.7 mm for deformations ranging from 13 to 33 mm, outperforming
the diffusion method. The TPS method, in part because it utilizes fiducial information, performed better than the
Laplace method, with mean TRE ranging from 0.3 to 1.9 mm for the same range of deformations. The Laplace and TPS
methods have the potential to be used by analyses requiring point correspondence between deforming surfaces.
This work extends a recently realized inverse problem technique of extracting soft tissue elasticity information via non-rigid model-based image registration. The algorithm uses the elastic properties of the tissue in a biomechanical model to achieve maximal similarity between image data acquired under different states of loading. A new multi-resolution, non-linear optimization framework has been employed which allows for improved performance and object detection. Prior studies have demonstrated successful reconstructions from images of a tissue-like thin membrane phantom with a single embedded inclusion that was significantly stiffer than its surroundings. For this investigation, a similar phantom was fabricated with two stiff inclusions to test the effectiveness of this method in discriminating multiple smaller objects. Elasticity values generated from both simulation and real data testing scenarios provided sufficient contrast for detection and good quantitative localization of the inclusion areas.
A significant amount of breast cancer research in recent years has been devoted to novel means of tumor detection such as MR contrast enhancement, electrical impedance tomography, microwave imaging, and elastography. Many of these detection methods involve deforming the breast. Often, these deformed images need to be correlated to anatomical images of the breast in a different configuration. In the case of our elastography framework, a series of comparisons between the pre- and post-deformed images needs to be performed. This paper presents an automatic method for determining correspondence between images of a pendant breast and a partially-constrained, compressed breast. The algorithm is an extension to the symmetric closest point approach of Papademetris et al. However, because of the unique deformation and shape change of a partially-constrained, compressed breast, the algorithm was modified through the use of iterative closest point (ICP) registration on easily identifiable sections of the breast images and through weighting the symmetric nearest neighbor correspondence. The algorithm presented in this paper significantly improves correspondence determination between the pre- and post-deformed images for a simulation when compared to the original Papademetris et al.'s symmetric closest point criteria.