Modality independent elastography (MIE): a new approach to elasticity imaging

MaterialCloning: Acquiring Elasticity Parameters from Images for Medical Applications

We present a practical approach for automatically estimating the material properties of soft bodies from two sets of images, taken before and after deformation. We reconstruct 3D geometry from the given sets of multiple-view images; we use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. For shape correspondence, we use a distance-based error metric to compare the estimated deformation fields against the actual deformation field from the reconstructed geometry. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple types of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. We demonstrate this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. We also highlight the results on physics-based animation of virtual objects using sketches from an artist's conception.

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was [Formula: see text]. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.

A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: theoretical framework and validation

Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.

A study of breast motion using non-linear dynamic FE analysis

This paper presents a new method to simulate non-linear breast motion by using a three-dimensional (3D) dynamic finite element model (FEM). The model consists of a thorax with two breasts and three skin layers with specific mechanical properties. Using free breast vibration, the viscous damping ratios were ascertained to be 0.215 for an 80B size breast. The shear modulus for the breast was derived as the value that gave the minimum difference between the FEM-predicted results and the experimental data. A hyper-elastic neo-Hookean material model simulated the large deformation of breast tissue. The mode shapes of breast motions at different natural frequencies were established. The highest breast displacement amplitude ratio relative to the thorax was at 4 Hz. The study showed that FEM can predict breast displacement with sufficient accuracy and thereby provide the basis by which bras may be engineered more ergonomically in the future.

PRACTITIONER SUMMARY

To facilitate a theoretical analysis of breast motion to enable the design of more supportive bras, a dynamic FEM based on reliable non-linear properties of breast tissues has been developed. The methods and findings have potential widespread benefit for developing new products to promote women's health and comfort.

Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound orMR images) and known external forces.Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

Development and evaluation of an identification method for the biomechanical parameters using robotic force measurements, medical images, and FEA

This paper presents a new identification method for the biomechanical parameters of human tissues for the purpose of improving the accuracy of dynamic organ simulation. We describe the formulation of the method, and also design a robotic system to implement the method using a robotic probe, a medical imaging device, and a numerical simulator for the finite element analysis (FEA). We carried out an experiment using an experimental system and a tissue phantom to verify the effectiveness of the method. The results of this experiment show that the Young's modulus of the tissue phantom can be estimated with the experimental system. We also compared the estimated values of the Young's moduli with the measured values from a rheometer. These results confirm that the identification method and the system design, proposed and developed in this work, are effective for accurately simulating organ behavior.

A general methodology for the determination of 2D bodies elastic deformation invariants: application to the automatic identification of parasites

A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances so as to quantify mechanoelastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechanoelastic properties of the bodies are stated which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot a deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both of these processes may furnish a body-undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers, and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites, and then, apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into six families, with an accuracy rate of 97.6 percent.

An inverse problem approach for elasticity imaging through vibroacoustics

A methodology for estimating the spatial distribution of elastic moduli using the steady-state dynamic response of solids immersed in fluids is presented. The technique relies on the ensuing acoustic field from a remotely excited solid to inversely estimate the spatial distribution of Young's modulus of biological structures (e.g., breast tissue). This work proposes the use of Gaussian radial basis functions (GRBF) to represent the spatial variation of elastic moduli. GRBF are shown to possess the advantage of representing smooth functions with quasi-compact support and can efficiently represent elastic moduli distributions such as those that occur in soft biological tissue in the presence of unhealthy tissue (e.g., tumors and calcifications). The direct problem consists of a coupled acoustic-structure interaction boundary-value problem solved in the frequency domain using the finite element method. The inverse problem is cast as an optimization problem in which the error functional is defined as a measure of discrepancy between an experimentally measured response and a finite element representation of the system. Nongradient based optimization algorithms are used to solve the resulting optimization problem. The feasibility of the proposed approach is demonstrated through a series of simulations and an experiment. For comparison purposes, the surface velocity response was also used for the inverse characterization as the measured response in place of the acoustic pressure.

Non-rigid registration of breast surfaces using the laplace and diffusion equations

A semi-automated, non-rigid breast surface registration method is presented that involves solving the Laplace or diffusion equations over undeformed and deformed breast surfaces. The resulting potential energy fields and isocontours are used to establish surface correspondence. This novel surface-based method, which does not require intensity images, anatomical landmarks, or fiducials, is compared to a gold standard of thin-plate spline (TPS) interpolation. Realistic finite element simulations of breast compression and further testing against a tissue-mimicking phantom demonstrate that this method is capable of registering surfaces experiencing 6 - 36 mm compression to within a mean error of 0.5 - 5.7 mm.

A finite element inverse analysis to assess functional improvement during the fracture healing process

Assessment of the restoration of load-bearing function is the central goal in the study of fracture healing process. During the fracture healing, two critical aspects affect its analysis: (1) material properties of the callus components, and (2) the spatio-temporal architecture of the callus with respect to cartilage and new bone formation. In this study, an inverse problem methodology is used which takes into account both features and yields material property estimates that can analyze the healing changes. Six stabilized fractured mouse tibias are obtained at two time points during the most active phase of the healing process, respectively 10 days (n=3), and 14 days (n=3) after fracture. Under the same displacement conditions, the inverse procedure estimations of the callus material properties are generated and compared to other fracture healing metrics. The FEA estimated property is the only metric shown to be statistically significant (p=0.0194) in detecting the changes in the stiffness that occur during the healing time points. In addition, simulation studies regarding sensitivity to initial guess and noise are presented; as well as the influence of callus architecture on the FEA estimated material property metric. The finite element model inverse analysis developed can be used to determine the effects of genetics or therapeutic manipulations on fracture healing in rodents.

Mechanical imaging of the breast

In this paper, we analyze the physical basis for elasticity imaging of the breast by measuring breast skin stress patterns that result from a force sensor array pressed against the breast tissue. Temporal and spatial changes in the stress pattern allow detection of internal structures with different elastic properties and assessment of geometrical and mechanical parameters of these structures. The method entitled mechanical imaging is implemented in the breast mechanical imager (BMI), a compact device consisting of a hand held probe equipped with a pressure sensor array, a compact electronic unit, and a touchscreen laptop computer. Data acquired by the BMI allows calculation of size, shape, consistency/hardness, and mobility of detected lesions. The BMI prototype has been validated in laboratory experiments on tissue models and in an ongoing clinical study. The obtained results prove that the BMI has potential to become a screening and diagnostic tool that could largely supplant clinical breast examination through its higher sensitivity, quantitative record storage, ease-of-use, and inherent low cost.

Elasticity reconstruction from displacement and confidence measures of a multi-compressed ultrasound RF sequence

Ultrasound elasticity imaging shows promise as a new way for early detection of cancers by assessing the elastic characteristics of soft tissue. So far the commonly used approach involves solving the so-called inverse elasticity problem of recovering elastic parameters from displacement measurements. We propose a finite-elementbased nonlinear scheme to estimate the elasticity distribution of soft tissue from multi-compressed ultrasound radio frequency (RF) data. An experimental ultrasound workstation has been developed to acquire multi-compressed data. A composite probe was employed as the compression plate. The contact forces and torques were acquired at the same time as imaging. Axial displacements under different static loads are estimated from the RF data before and after deformation using a cross-correlation technique. The confidence of displacement estimates is employed as a weighting factor in solving the objective function describing the inverse elasticity reconstruction problem. A novel splitand- merge strategy is employed over the image sequence in which strain images are used to provide a priori knowledge of the relative stiffness distribution of the tissue to constrain the inverse problem solution. The experimental study has allowed us to investigate the performance of our approach in the controlled environment of simulated and phantom data. For a simulated single inclusion model with 5% axial displacement estimation error, the L2-error between the target and the reconstructed Young's modulus was found to be about 1%. In vivo validation of the proposed method has been carried out and some preliminary results are presented.

Development of a mechanical testing assay for fibrotic murine liver

In this article, a novel protocol for mechanical testing, combined with finite element modeling, is presented that allows the determination of the elastic modulus of normal and fibrotic murine livers and is compared to an independent mechanical testing method. The novel protocol employs suspending a portion of murine liver tissue in a cylindrical polyacrylamide gel, imaging with a microCT, conducting mechanical testing, and concluding with a mechanical property determination via a finite element method analysis. More specifically, the finite element model is built from the computerized tomography (CT) images, and boundary conditions are imposed in order to simulate the mechanical testing conditions. The resulting model surface stress is compared to that obtained during mechanical testing, which subsequently allows for direct evaluation of the liver modulus. The second comparison method involves a mechanical indentation test performed on a remaining liver lobe for comparison. In addition, this lobe is used for histological analysis to determine relationships between elasticity measurements and tissue health. This complete system was used to study 14 fibrotic livers displaying advanced fibrosis (injections with irritant), three control livers (injections without irritant), and three normal livers (no injections). The moduli evaluations for nondiseased livers were estimated as 0.62 +/- 0.09 kPa and 0.59 +/- 0.1 kPa for indenter and model-gel-tissue (MGT) assay tests, respectively. Moduli estimates for diseased liver ranged from 0.6-1.64 kPa and 0.96-1.88 kPa for indenter and MGT assay tests, respectively. The MGT modulus, though not equivalent to the modulus determined by indentation, demonstrates a high correlation, thus indicating a relationship between the two testing methods. The results also showed a clear difference between nondiseased and diseased livers. The developed MGT assay system is quite compact and could easily be utilized for controlled evaluation of soft-tissue moduli as shown here. In addition, future work will add the correlative method of elastography such that direct controlled validation of measurement on tissue can be determined.

Evaluation of 3D modality-independent elastography for breast imaging: a simulation study

This paper reports on the development and preliminary testing of a three-dimensional implementation of an inverse problem technique for extracting soft-tissue elasticity information via non-rigid model-based image registration. The modality-independent elastography (MIE) algorithm adjusts the elastic properties of a biomechanical model to achieve maximal similarity between images acquired under different states of static loading. A series of simulation experiments with clinical image sets of human breasts were performed to test the ability of the method to identify and characterize a radiographically occult stiff lesion. Because boundary conditions are a critical input to the algorithm, a comparison of three methods for semi-automated surface point correspondence was conducted in the context of systematic and randomized noise processes. The results illustrate that 3D MIE was able to successfully reconstruct elasticity images using data obtained from both magnetic resonance and x-ray computed tomography systems. The lesion was localized correctly in all cases and its relative elasticity found to be reasonably close to the true values (3.5% with the use of spatial priors and 11.6% without). In addition, the inaccuracies of surface registration performed with thin-plate spline interpolation did not exceed empiric thresholds of unacceptable boundary condition error.

Detecting mechanical abnormalities in prostate tissue using FE-based image registration

An image registration-based elastography algorithm is presented for assessing the stiffness of tissue regions inside the prostate for the purpose of detecting tumors. A 3D finite-element model of the prostate is built from ultrasound images and used to simulate the deformation of the prostate induced by a TRUS probe. To reconstruct the stiffness of tissues, their Young's moduli are varied using Powell's method so that the mutual information between a simulated and deformed image volume is maximized. The algorithm was validated using a gelatin prostate phantom embedded with a cylindrical inclusion that simulated a tumor. Results from the phantom study showed that the technique could detect the increased stiffness of the simulated tumor with a reasonable accuracy.

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Computational elastography from standard ultrasound image sequences by global trust region optimization

A new approach is proposed to estimate the spatial distribution of shear modulus of tissues in-vivo. An image sequence is acquired using a standard medical ultrasound scanner while varying the force applied to the handle. The elastic properties are then recovered simultaneously with the inter-frame displacement fields using a computational procedure based on finite element modeling and trust region constrained optimization. No assumption about boundary conditions is needed. The optimization procedure is global, taking advantage of all available images. The algorithm was tested on phantom, as well as on real clinical images.

Prostate mechanical imaging: 3-D image composition and feature calculations

We have developed a method and a device entitled prostate mechanical imager (PMI) for the real-time imaging of prostate using a transrectal probe equipped with a pressure sensor array and position tracking sensor. PMI operation is based on measurement of the stress pattern on the rectal wall when the probe is pressed against the prostate. Temporal and spatial changes in the stress pattern provide information on the elastic structure of the gland and allow two-dimensional (2-D) and three-dimensional (3-D) reconstruction of prostate anatomy and assessment of prostate mechanical properties. The data acquired allow the calculation of prostate features such as size, shape, nodularity, consistency/hardness, and mobility. The PMI prototype has been validated in laboratory experiments on prostate phantoms and in a clinical study. The results obtained on model systems and in vivo images from patients prove that PMI has potential to become a diagnostic tool that could largely supplant DRE through its higher sensitivity, quantitative record storage, ease-of-use and inherent low cost.

Factors influencing the accuracy of biomechanical breast models

Recently it has been suggested that finite element methods could be used to predict breast deformations in a number of applications, including comparison of multimodality images, validation of image registration and image guided interventions. Unfortunately knowledge of the mechanical properties of breast tissues is limited. This study evaluated the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast in the practical clinical situation where the boundaries of the organ might be known reasonably accurately but there is some uncertainty on the mechanical properties of the tissue. For two datasets, we investigate the influence of tissue elasticity values, Poisson's ratios, boundary conditions, finite element solvers and mesh resolutions. Magnetic resonance images were acquired before and after compressing each volunteer's breast by about 20%. Surface displacement boundary conditions were derived from a three-dimensional nonrigid image registration. Six linear and three nonlinear elastic material models with and without skin were tested. These were compared to hyperelastic models. The accuracy of the models was evaluated by assessing the ability of the model to predict the location of 12 corresponding anatomical landmarks. The accuracy was most sensitive to the Poisson's ratio and the boundary condition. Best results were achieved for accurate boundary conditions, appropriate Poisson's ratios and models where fibroglandular tissue was at most four times stiffer than fatty tissue. These configurations reduced the mean (maximum) distance of the landmarks from 6.6 mm (12.4 mm) to 2.1 mm (3.4 mm) averaged over all experiments.

Ultrasound elasticity imaging

Before the advent of diagnostic imaging, palpation was one of the main methods of clinical investigation for the evaluation of tumours. Malignant tumours feel harder that benign ones and this physical property is related to their coefficient of elasticity. Direct comparison of tissue images before and after application of a force is too crude a measure of elasticity except at extremes of differences in elasticity. Analysis of the raw imaging data, which contains very much more information than can be displayed for visual perception, can detect very much smaller differences in elasticity.

The radio frequency data of returning ultrasound echoes contain much more data than appears in an ultrasound image. Comparison, of the datasets of uncompressed tissue with compressed tissue, of a region of interest allows production of a strain (elasticity) image of that same region of interest. Change in tissue which is not visible on B-mode (greyscale) imaging can now be detected with real time strain imaging which is beginning to be developed on commercial ultrasound equipment. The information obtained with strain/elasticity imaging is now showing potential in influencing management of patients with breast problems.

Modality independent elastography (MIE): a new approach to elasticity imaging

The correlation between tissue stiffness and health is an accepted form of organ disease assessment. As a result, there has been a significant amount of interest in developing methods to image elasticity parameters (i.e., elastography). The modality independent elastography (MIE) method combines a nonlinear optimization framework, computer models of soft-tissue deformation, and standard measures of image similarity to reconstruct elastic property distributions within soft tissue. In this paper, simulation results demonstrate successful elasticity image reconstructions in breast cross-sectional images acquired from magnetic resonance (MR) imaging. Results from phantom experiments illustrate its modality independence by reconstructing elasticity images of the same phantom in both MR and computed tomographic imaging units. Additional results regarding the performance of a new multigrid strategy to MIE and the implementation of a parallel architecture are also presented.