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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. Index Terms--Density measurement, elasticity reconstruction, MR elastography, tissue density, tissue mechanics.

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. Index Terms--Breast cancer detection, elasticity imaging, elastography, inverse elasticity problem, magnetic resonance imaging, parallel computing, shear modulus estimation.

The contrast-to-noise ratio (CNR) 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 (ROI) detection. OCIS codes: 170.6960, 170.7050, 110.2960, 110.3000, 110.6960, 170.3830.

Objective: Tumor stiffening in pancreatic adenocarcinoma (PDAC) has been linked to cancer progression and lack of therapy response, yet current elastography tools cannot map stiffness in a whole tumor field-of-view with biologically relevant spatial resolution. Therefore, this study was developed to assess stiffness heterogeneity and geometrical patterns across whole PDAC xenograft ex vivo tumors. Methods: The ex vivo elastography (EVE) mapping system was capable of creating stiffness map at 300-micron spatial resolution under a 5–20 mm field of view relevant to whole tumor assessment. The stiffness value at each location was determined by compression testing and an absolute tumor Young's modulus map was calculated based on the calibration between the system and ultrasound elastography (R2 = 0.95). Results: Two PDAC tumor lines AsPC-1 and BxPC-3 implanted in xenograft models were assessed to show tumor stiffness and its linear relationship to collagen content (R2 = 0.59). EVE was able to capture stiffness heterogeneity ranging between 5 and 100 kPa in pancreatic tumors with collagen content up to 25%. More importantly, data shows the inverse relationship of local stiffness to local drug distribution (R2 = 0.66) and vessel patency (R2 = 0.61) in both PDAC tumor lines. Conclusion: The results suggested that elastography could be utilized to predict drug penetration in PDAC tumors or assess response to biological modifying adjunct therapies. Significance: This study presents the first attempt to map out stiffness on a biologically relevant spatial scale across whole PDAC tumor slices with spatial resolution in the hundreds of microns. [ABSTRACT FROM AUTHOR]