After 20 years of innovation in techniques that specifically image the biomechanical properties of tissue, the evolution of elastographic imaging can be viewed from its infancy, through a proliferation of approaches to the problem to incorporation on research and then clinical imaging platforms. Ultimately this activity has culminated in clinical trials and improved care for patients. This remarkable progression represents a leading example of translational research that begins with fundamentals of science and engineering and progresses to needed improvements in diagnostic and monitoring capabilities applied to major categories of disease, surgery and interventional procedures. This review summarizes the fundamental principles, the timeline of developments in major categories of elastographic imaging, and concludes with recent results from clinical trials and forward-looking issues.

Elastography is emerging as an imaging modality that can distinguish normal versus diseased tissues via their biomechanical properties. This article reviews current approaches to elastography in three areas — quasi-static, harmonic, and transient — and describes inversion schemes for each elastographic imaging approach. Approaches include: first-order approximation methods; direct and iterative inversion schemes for linear elastic; isotropic materials; and advanced reconstruction methods for recovering parameters that characterize complex mechanical behavior. The paper’s objective is to document efforts to develop elastography within the framework of solving an inverse problem, so that elastography may provide reliable estimates of shear modulus and other mechanical parameters. We discuss issues that must be addressed if model-based elastography is to become the prevailing approach to quasi-static, harmonic, and transient elastography: (1) developing practical techniques to transform the ill-posed problem with a well-posed one; (2) devising better forward models to capture the transient behavior of soft tissue; and (3) developing better test procedures to evaluate the performance of modulus elastograms.

This paper describes an inverse reconstruction technique based on a modified Newton Raphson iterative scheme and the finite element method, which has been developed for computing the spatial distribution of Young's modulus from within soft tissues. Computer simulations were conducted to determine the relative merits of reconstructing tissue elasticity using knowledge of (a) known displacement boundary conditions (DBC), and (b) known stress boundary conditions (SBC). The results demonstrated that computing Young's modulus using knowledge of SBC allows accurate quantification of Young's modulus. However, the quality of the images produced using this reconstruction approach was dependent on the Young's modulus distribution assumed at the start of the reconstruction procedure. Computing Young's modulus from known DBC provided relative estimates of tissue elasticity which, despite the disadvantage of not being able to accurately quantify Young's modulus, formed images that were generally superior in quality to those produced using the known SBC, and were not affected by the trial solution. The results of preliminary experiments on phantoms demonstrated that this reconstruction technique is capable in practice of improving the fidelity of tissue elasticity images, reducing the artefacts otherwise present in strain images, and recovering Young's modulus images that possess excellent spatial and contrast resolution.

Recently a new adjoint equation based iterative method was proposed for evaluating the spatial distribution of the elastic modulus of tissue based on the knowledge of its displacement field under a deformation. In this method the original problem was reformulated as a minimization problem, and a gradient-based optimization algorithm was used to solve it. Significant computational savings were realized by utilizing the solution of the adjoint elasticity equations in calculating the gradient. In this paper, we examine the performance of this method with regard to measures which we believe will impact its eventual clinical use. In particular, we evaluate its abilities to (1) resolve geometrically the complex regions of elevated stiffness; (2) to handle noise levels inherent in typical instrumentation; and (3) to generate three-dimensional elasticity images. For our tests we utilize both synthetic and experimental displacement data, and consider both qualitative and quantitative measures of performance. We conclude that the method is robust and accurate, and a good candidate for clinical application because of its computational speed and efficiency.

Purpose: To describe initial in vivo experiences with a subzone-based, steady-state MR elastography (MRE) method. This sparse collection of in vivo results is intended to shed light on some of the strengths and weaknesses of existing clinical MRE approaches and to indicate important areas of future research. Materials and Methods:Elastic property reconstruction results are compared with data compiled from the limited existing body of published studies in breast elasticity. Mechanical parameter distributions are also investigated in terms of their implications for the nature of biological soft tissue. Additionally, a derivation of the statistical variance of the elastic parameter reconstruction is given and the resulting confidence intervals (CIs) for different parameter solutions are examined.Results: By comparison with existing estimates of the elastic properties of breast tissue, the subzone-based, steadystate MRE method is seen to produce reasonable estimates for the mechanical properties of in vivo tissue. Conclusion:MRE shows potential as an effective way to determine the elastic properties of breast tissue, and may be of significant clinical interest.

The design and implementation of a multispectral, frequency-domain near infrared tomography system is outlined, which operates in a MRI magnet for utilization of MR-guided image reconstruction of tissue optical properties. Using long silica optical fiber bundles, measurements of light transmission through up to 12 cm of female breast tissue can be acquired simultaneously with MRI scans. The NIR system utilizes six optical wavelengths from 660 to 850 nm using intensity modulated diode lasers nominally working at 100 MHz. Photomultiplier tube detector gain levels are electronically controlled on a time scale of 200 ms, thereby allowing rapid switching of the source to locations around the tissue. There are no moving parts in the detection channels and for each source position, 15 PMTs operating in parallel allow sensitivity down to 0.5 pW/cm2 at the tissue surface. Images of breast tissue optical absorption and reduced scattering coefficients are obtained using a Newton-type reconstruction algorithm to solve for an optimal solution using the measurement data. In medical imaging, it is beneficial to compare the same tissue volume as seen by a variety of modalities, and perhaps more importantly, there is the hypothesis that one imaging system which has high spatial resolution can be used to enhance the reconstruction of another system which has good contrast resolution. In this study we explore the synergistic benefits of a combined NIR-MRI data set, specifically the ways in which MRI (i.e., high spatial resolution) enhances NIR (i.e., high contrast resolution) image reconstruction. The design, calibration, and performance of the imaging system are described in the context of preliminary phantom tests and initial in vivo patient imaging. Co-registered MRI validates and improves optical property estimation in 2D tomographic image reconstructions when specialized algorithms are used.

The contrast-to-noise ratio ͑CNR͒ was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest ͑ROI͒ detection.

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