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Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Noninvasive imaging tools that better characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors which lead to the development of unstable lesions, and identify patients at risk of plaque disruption and stroke. Carotid magnetic resonance (MR) imaging allows for the characterization of carotid vessel wall and plaque composition, the characterization of normal and pathological arterial wall, the quantification of plaque size, and the detection of plaque integrity. On the other hand, various ultrasound (US) measurements have also been used to quantify atherosclerosis, carotid stenosis, intima-media thickness, total plaque volume, total plaque area, and vessel wall volume. Combining the complementary information provided by 3D MR and US carotid images may lead to a better understanding of the underlying compositional and textural factors that define plaque and wall vulnerability, which may lead to better and more effective stroke prevention strategies and patient management. Combining these images requires nonrigid registration to correct the nonlinear misalignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisition sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, a "twisting and bending" model was used with only six parameters to model the normal movement of the neck for nonrigid registration. The registration technique was evaluated using 3D US and MR carotid images at two field strengths, 1.5 and 3.0 T, of the same subject acquired on the same day. The mean registration error between the segmented carotid artery wall boundaries in the target US image and the registered MR images was calculated using a distance-based error metric after applying a "twisting and bending" model based nonrigid registration algorithm. An average registration error of 1.4 +/- 0.3 mm was obtained for 1.5 T MR and 1.5 +/- 0.4 mm for 3.0 T MR, when registered with 3D US images using the nonrigid registration technique presented in this paper. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with this nonrigid registration technique compared to rigid registration.

This work presents the initial development and implementation of a novel 3D biomechanics-based approach to measure the mechanical activity of myocardial tissue, as a potential non-invasive tool to assess myocardial function. This technique quantifies the myocardial contraction forces developed within the ventricular myofibers in response to electro-physiological stimuli. We provide a 3D finite element formulation of a contraction force reconstruction algorithm, along with its implementation using magnetic resonance (MR) data. Our algorithm is based on an inverse problem solution governed by the fundamental continuum mechanics principle of conservation of linear momentum, under a first-order approximation of elastic and isotropic material conditions. We implemented our technique using a subject-specific ventricle model obtained by extracting the left ventricular anatomical features from a set of high-resolution cardiac MR images acquired throughout the cardiac cycle using prospective electrocardiographic gating. Cardiac motion information was extracted by non-rigid registration of the mid-diastole reference image to the remaining images of a 4D dataset. Using our technique, we reconstructed dynamic maps that show the contraction force distribution superimposed onto the deformed ventricle model at each acquired frame in the cardiac cycle. Our next objective will consist of validating this technique by showing the correlation between the presence of low contraction force patterns and poor myocardial functionality.

MR elastography (MRE) is an MRI modality that is increasingly being used to image tissue elasticity throughout the body. One MRE technique that has received a great deal of attention is based on visualizing shear waves, which reveal stiffness by virtue of their local wavelength. However, the shape of propagating shear waves can also provide valuable information about the nonlinear stress-strain behavior of tissue. Here an experiment is proposed that allows the observation of nonlinear wave propagation based on spatial-temporal phase contrast images. A theoretical description of the wave propagation was developed that reflects typical MRE excitation, which involves excitation modes both parallel and perpendicular to B0. Based on this model, it is shown that both odd and even higher harmonics are produced with their amplitudes dependent on the details of the actuator, imaging geometry, and the nonlinear tissue properties. With appropriate motion encoding, harmonic vibrations arising from nonlinear tissue response can be detected. The effect is demonstrated on an agarose gel phantom using a sinusoidal shear vibration of 150 Hz, and clearly shows the presence of harmonics at 600 and 750 Hz. Using an estimate of the strain energy of the phantom, we were able to determine the nonlinear tissue properties.

Over the past decade, several methods have been proposed to image tissue elasticity based on imaging methods collectively called elastography. While progress in developing these systems has been rapid, the basic understanding of tissue properties to interpret elastography images is generally lacking. To address this limitation, we developed a system to measure the Young's modulus of small soft tissue specimens. This system was designed to accommodate biological soft tissue constraints such as sample size, geometry imperfection and heterogeneity. The measurement technique consists of indenting an unconfined small block of tissue while measuring the resulting force. We show that the measured force-displacement slope of such a geometry can be transformed to the tissue Young's modulus via a conversion factor related to the sample's geometry and boundary conditions using finite element analysis. We also demonstrate another measurement technique for tissue elasticity based on quasi-static magnetic resonance elastography in which a tissue specimen encased in a gelatine-agarose block undergoes cyclical compression with resulting displacements measured using a phase contrast MRI technique. The tissue Young's modulus is then reconstructed from the measured displacements using an inversion technique. Finally, preliminary elasticity measurement results of various breast tissues are presented and discussed.

A study of the applicability of nonlinear finite element analysis (FEA) to predict soft tissue deformation was validated with phase contrast magnetic resonance velocity imaging. A phantom of varying stiffness was placed in a special purpose, computer controlled MR compatible compression apparatus which provided precise, time varying compression with surface deformations on the order of 11%. The resulting motion was measured with MR velocity images acquired throughout the cycle of compression. The phantom geometry was modeled with a finite element mesh and the mechanical properties of the phantom material were measured and incorporated in the FEA model. The motion as calculated by the FEA model was compared to the motion measured with MRI and the results were found to vary with the material's Poisson's ratio and the coefficient of friction. A minimum difference was reached when the Poisson's ratio and coefficient of friction were set to 0.485 and 0.3, respectively. Under these conditions, the root mean square difference was found to be 14.4%.

Purpose: Association between tissue stiffness alteration and pathology is well known. This has formed the basis for prostate elastography imaging techniques where images of prostate tissue mechanical properties are reconstructed. In this paper, the authors present a novel prostate elastography technique which, unlike other techniques, relies on magnitude image data only. Methods: This proposed technique works in conjunction with ultrasound or magnetic resonance imaging (MRI) imaging modalities and it requires the prostate's pre- and postdeformation images as input. It uses a constrained reconstruction method where the elastic moduli of the prostate's normal and pathological tissues are determined based on an essential subset of the tissue deformation provided by the images data. The elasticity reconstruction technique uses optimization where similarity between calculated and observed shape features of the postcompression prostate image is maximized. The method was validated with an in silico phantom study followed by studies using ultrasound and MR with tissue-mimicking phantoms. Results: Using the proposed methods, it was observed that the maximum uncertainties of the reconstructed Young's modulus ratios of tumor to normal tissue were 15.6% and 9.7%, which were obtained from the transrectal ultrasound (TRUS) and MR tissue-mimicking phantom studies, respectively. Conclusions: This novel prostate elastography technique relies on prostate TRUS or MRI images that can be routinely acquired without additional imaging hardware. The phantom studies provided evidence that the proposed technique has a good potential to reconstruct prostate stiffness maps noninvasively particularly when applied in conjunction with MRI. Further studies are necessary to evaluate the technique's merits for clinical use. [ABSTRACT FROM AUTHOR]

Presents a study which analyzed the signal/noise ratio of strain images that can be produced by static or quasi-static magnetic resonance elastography. Methods; Results; Discussion.

Focuses on the three-dimensional finite element modeling of breast tissue deformation through the use of magnetic resonance imaging. Significance of finite element modeling of breast deformation; Evaluation of breast deformation models; Discussion on the different aspects of breast deformation.

Compressibility of biological tissues such as brain parenchyma is related to its poroelastic nature characterized by the geometry and pressure of vasculature and interconnected fluid-filled spaces. Thus, cerebral volumetric strain may be sensitive to intracranial pressure which can be altered under physiological conditions. So far volumetric strain has attained little attention in studies of the mechanical behavior of the brain. This paper reports a study of measuring the in vivo cerebral volumetric strain induced by the Valsalva maneuver (VM) where forced expiration against a closed glottis leads to a transient increase in the intracranial pressure. For this purpose we applied three-dimensional magnetic resonance imaging equipped with a patient-controlled acquisition system to five healthy volunteers. With each volunteer, three experiments were performed: one with VM and two in resting state. i.e. normal ventilation, which were conducted before and after VM. The VM data were registered to reference data by morphology based non-rigid deformation, yielding 3D maps of total displacements and volumetric strain. On average, VM induced volumetric strain correlated to whole-brain dilatation of - 3.14 ± 0.87% and - 2.80 ± 0.71% compared to the reference states before and after VM, respectively. These values were well reproduced by repetitive experiments during the same scan as well as by repeated measurements in one volunteer on different days. Combined with literature data of intracranial pressure changes, our volumetric strain values can be used to elucidate the static compression modulus of the in vivo human brain. These results add knowledge to the understanding of the brain's biomechanical properties under physiological conditions. [ABSTRACT FROM AUTHOR]