Magnetic resonance elastography (MRE) has been developed to noninvasively reconstruct mechanical properties for tissue and tissue-like materials over a frequency range of 10 ~200 Hz. In this work, low frequency (1~1.5 Hz) MRE activations were employed to estimate mechanical property distributions of simulated data and experimental phantoms. Nonlinear inversion (NLI) MRE algorithms based on viscoelastic and poroelastic material models were used to solve the inverse problems and recover images of the shear modulus and hydraulic conductivity. Data from a simulated phantom containing an inclusion with property contrast was carried out to study the feasibility of our low frequency actuated approach. To verify the stability of NLI algorithms for low frequency actuation, different levels of synthetic noise were added to the displacement data. Spatial distributions and property values were recovered well for noise level less than 5%. For the presented experimental phantom reconstructions with regularizations, the computed storage moduli from viscoelastic and poroelastic MRE gave similar results. Contrast was detected between inclusions and background in recovered hydraulic conductivity images. Results and findings confirm the feasibility of future in vivo neuroimaging examinations using natural cerebrovascular pulsations at cardiac frequencies, which can eliminate specialized equipment for high frequency actuation.

We describe an efficient gradient computation for solving inverse problems arising in magnetic resonance elastography (MRE). The algorithm can be considered as a generalized 'adjoint method' based on a Lagrangian formulation. One requirement for the classic adjoint method is assurance of the self-adjoint property of the stiffness matrix in the elasticity problem. In this paper, we show this property is no longer a necessary condition in our algorithm, but the computational performance can be as efficient as the classic method, which involves only two forward solutions and is independent of the number of parameters to be estimated. The algorithm is developed and implemented in material property reconstructions using poroelastic and viscoelastic modeling. Various gradient- and Hessian-based optimization techniques have been tested on simulation, phantom and in vivo brain data. The numerical results show the feasibility and the efficiency of the proposed scheme for gradient calculation.

Poroelastic magnetic resonance elastography is an imaging technique that could recover mechanical and hydrodynamical material properties of in vivo tissue. To date, mechanical properties have been estimated while hydrodynamical parameters have been assumed homogeneous with literature-based values. Estimating spatially-varying hydraulic conductivity would likely improve model accuracy and provide new image information related to a tissue's interstitial fluid compartment. A poroelastic model was reformulated to recover hydraulic conductivity with more appropriate fluid-flow boundary conditions. Simulated and physical experiments were conducted to evaluate the accuracy and stability of the inversion algorithm. Simulations were accurate (property errors were < 2%) even in the presence of Gaussian measurement noise up to 3%. The reformulated model significantly decreased variation in the shear modulus estimate (p << 0.001) and eliminated the homogeneity assumption and the need to assign hydraulic conductivity values from literature. Material property contrast was recovered experimentally in three different tofu phantoms and the accuracy was improved through soft-prior regularization. A frequency-dependence in hydraulic conductivity contrast was observed suggesting that fluid-solid interactions may be more prominent at low frequency. In vivo recovery of both structural and hydrodynamical characteristics of tissue could improve detection and diagnosis of neurological disorders such as hydrocephalus and brain tumors.

Tissue displacements required for mechanical property reconstruction in magnetic resonance elastography (MRE) are acquired in a magnetic resonance imaging (MRI) scanner, therefore, anatomical information is available from other imaging sequences. Despite its availability, few attempts to incorporate prior spatial information in the MRE reconstruction process have been reported. This paper implements and evaluates soft prior regularization (SPR), through which homogeneity in predefined spatial regions is enforced by a penalty term in a nonlinear inversion strategy. Phantom experiments and simulations show that when predefined regions are spatially accurate, recovered property values are stable for SPR weighting factors spanning several orders of magnitude, whereas inaccurate segmentation results in bias in the reconstructed properties that can be mitigated through proper choice of regularization weighting. The method was evaluated in vivo by estimating viscoelastic mechanical properties of frontal lobe gray and white matter for five repeated scans of a healthy volunteer. Segmentations of each tissue type were generated using automated software, and statistically significant differences between frontal lobe gray and white matter were found for both the storage modulus and loss modulus . Provided homogeneous property assumptions are reasonable, SPR produces accurate quantitative property estimates for tissue structures which are finer than the resolution currently achievable with fully distributed MRE.

Magnetic resonance poroelastography (MRPE) is introduced as an alternative to single-phase model-based elastographic reconstruction methods. A 3-D finite element poroelastic inversion algorithm was developed to recover the mechanical properties of fluid-saturated tissues. The performance of this algorithm was assessed through a variety of numerical experiments, using synthetic data to probe its stability and sensitivity to the relevant model parameters. Preliminary results suggest the algorithm is robust in the presence of noise and capable of producing accurate assessments of the underlying mechanical properties in simulated phantoms. Furthermore, a 3-D time-harmonic motion field was recorded for a poroelastic phantom containing a single cylindrical inclusion and used to assess the feasibility of MRPE image reconstruction from experimental data. The elastograms obtained from the proposed poroelastic algorithm demonstrate significant improvement over linearly elastic MRE images generated using the same data. In addition, MRPE offers the opportunity to estimate the time-harmonic pressure field resulting from tissue excitation, highlighting the potential for its application in the diagnosis and monitoring of disease processes associated with changes in interstitial pressure.

A reconstruction process featuring full parameterization of the three dimensional, time-harmonic equations of linear elasticity is developed and reconstructed property images are presented from simulation-based investigation. While interesting in its own right through the potential for increased adaptability of these reconstructive elastic imaging techniques, this study also presents a set of analysis tools used to study the poor convergence behavior found in the case of tissue like conditions (i.e. nearly incompressible materials). The choice of elastic properties for imaging in elastography research remains an open question at this point; the use of the stability and sensitivity-based analytical methods described here will help to predict and understand the value and reliability of different parameterizations of elasticity imaging. Additionally, though results indicate significant work needs to be done to achieve effective multiparameter reconstructive imaging, the methods detailed here offer the promise of increased flexibility and sophistication in elastographic imaging techniques.

Magnetic resonance elastography can be limited by the computationally intensive nonlinear inversion schemes that are sometimes employed to estimate shear modulus from externally induced internal tissue displacements. Consequently, we have developed a parallelized partial volume reconstruction approach to overcome this limitation. In this paper, we report results from experiments conducted on breast phantoms and human volunteers to validate the proposed technique. More specifically, we demonstrate that computational cost is linearly related to the number of subzones used during image recovery and that both subzone parallelization and partial volume domain reduction decrease execution time accordingly. Importantly, elastograms computed based on the parallelized partial volume technique are not degraded in terms of either image quality or accuracy relative to their full volume counterparts provided that the estimation domain is sufficiently large to negate the effects of boundary conditions. The clinical results presented in this paper are clearly preliminary; however, the parallelized partial volume reconstruction approach performs sufficiently well to warrant more in-depth clinical evaluation.

The use of arrays and records in modern hardware-description languages (HDL) allows designs to be modeled at very high levels of abstraction. However, the support for these complex data types in current synthesis tools is very limited. This paper presents a comprehensive scheme to synthesize aggregate data types such as arrays and records, in a very general manner. The approach consists of mapping objects (variables and signals) of aggregate data types onto one-dimensional vectors, and generating specialized addressing/decoding hardware to be able to access any field of the data type. Arrays of multiple dimensions and any level of nesting of arrays and records are supported. This paper describes the whole process, from the language specification to the actual hardware structures created by synthesis.

Discusses the advantages and disadvantages of using a Karhunen-Loeve (K-L) expansion of a training set of images to reduce the number of encodes required for a magnetic resonance (MR) image of a new object. One form of this technique has been proposed [Cao and Levin, 1993] and another implemented [Zientara et al., 1994]. The authors evaluate the error likely to be achieved as a function of the number of encodes and two technical problems: reduced SNR in the images and smoothing of the K-L functions in practice. As an alternative, they propose the use of joint best bases [Wickerhauser, 1994] derived from the local trigonometric library as an approximation to the K-L basis. These bases approach the rate-distortion characteristic achieved by the K-L basis, but they are easier to use in MRI and can be applied with existing methods for fast acquisition.

Examines the advantages and disadvantages of using a Karhunen-Loeve (K-L) expansion of a training set of images to reduce the number of encodes required for a magnetic resonance (MR) image of a new object. One form of this technique has been proposed (Yue Cao and Levin, 1993) and another implemented (Zientara et al., 1994). The present authors evaluate: a) the error likely to be achieved as a function of the number of encodes and b) two technical problems: reduced SNR in the images and smoothing of the K-L functions in practice. They propose the use of localized trigonometric bases developed by Coifman and Wickerhauser (1992) as an alternative to the K-L basis. The localized trigonometric bases approach the error achieved by the K-L basis, but they are easier to use and can be used with existing methods for fast acquisition.>

Wavelet transforms are multiresolution decompositions that can be used to analyze signals and images. They describe a signal by the power at each scale and position. Edges can be located very effectively in the wavelet transform domain. A spatially selective noise filtration technique based on the direct spatial correlation of the wavelet transform at several adjacent scales is introduced. A high correlation is used to infer that there is a significant feature at the position that should be passed through the filter. The authors have tested the technique on simulated signals, phantom images, and real MR images. It is found that the technique can reduce noise contents in signals and images by more than 80% while maintaining at least 80% of the value of the gradient at most edges. The authors did not observe any Gibbs' ringing or significant resolution loss on the filtered images. Artifacts that arose from the filtration are very small and local. The noise filtration technique is quite robust. There are many possible extensions of the technique. The authors see its applications in spatially dependent noise filtration, edge detection and enhancement, image restoration, and motion artifact removal. They have compared the performance of the technique to that of the Weiner filter and found it to be superior.

The authors describe two different applications of wavelet transforms in magnetic resonance imaging (MRI). In each case, the use of wavelets offers tangible benefits over the traditional Fourier-transform-based imaging. These stem from the simultaneous time and frequency localization properties of the wavelets. The first application utilizes the localization properties of wavelets to acquire single T/sub 2/ weighted images in times which are short relative to current methods. An interesting combinatorial scheduling problem arises from the multiscale structure inherent in the formulation of this procedure. Its solution is sketched and applied to the imaging process. The second example is a technique for potentially 'instant' imaging. Image acquisition is modeled as an inverse wavelet transform of the spin density in the region in question. Signal acquisition takes place in as few as one or two echoes after the excitation by RF pulses designed to excite a moving band of spins in a slice. These sweeping RF pulses excite strips of spins which rephase at different times during acquisition. They replace phase-encoding gradients; no fast pulsed gradients are required.>