Objective. The correlation between various diseases and the change in the local mechanical properties of soft tissues has been long known. Over the past 20 years, there have been increasing research efforts to characterize mechanical properties of biological tissues using ultrasonic elastography. However, most of these works were based on characterization of only 1 type of waves (longitudinal or shear). The goal of this work was to devise a comprehensive ultrasound-based imaging method capable of measuring elastic parameters by combining both backscattered elastography and throughtransmitted ultrasonic computed tomography. Methods. Our suggested technique provides measurements of both longitudinal and shear wave velocities. This enables the noninvasive computation of several tissue elasticity parameters such as Young's and shear moduli, Poisson's ratio, and, more importantly, the bulk modulus, the determination of which requires both wave velocities. Four different phantom types were examined: agar-gelatin-based phantoms and porcine fat tissue, turkey breast tissue, and bovine liver tissue in vitro specimens. The values of Young's modulus, the shear modulus, and Poisson's ratio were estimated and were consistent with values published in the literature. Results. The average bulk modulus values of the phantoms ± SD were 2.83 ± 0.001, 2.25 ± 0.01, 2.48 ± 0.01, and 2.53 ± 0.02 GPa, respectively. A statistically significant difference (P < .001) in the values of the bulk modulus of the different phantoms was found. Conclusions. The bulk modulus is suitable for differentiation between different tissue types. The obtained results show the feasibility of using a comprehensive ultrasonic imaging technique for noninvasive quantitative tissue characterization. Key words: bulk modulus; computed tomography; elastic parameters; elastography; tissue characterization; wave velocity. 2 However, in many cases, the small size of a pathologic lesion or its location deep in the body makes its detection and evaluation by palpation difficult or impossible. Furthermore, palpation is nonquantitative and lacks accuracy in locating the lesion.
Diffusion imaging coupled with tractography algorithms allows researchers to image human white matter fiber bundles in-vivo. These bundles are three-dimensional structures with shapes that change over time during the course of development as well as in pathologic states. While most studies on white matter variability focus on analysis of tissue properties estimated from the diffusion data, e.g. fractional anisotropy, the shape variability of white matter fiber bundle is much less explored. In this paper, we present a set of tools for shape analysis of white matter fiber bundles, namely: (1) a concise geometric model of bundle shapes; (2) a method for bundle registration between subjects; (3) a method for deformation estimation. Our framework is useful for analysis of shape variability in white matter fiber bundles. We demonstrate our framework by applying our methods on two datasets: one consisting of data for 6 normal adults and another consisting of data for 38 normal children of age 11 days to 8.5 years. We suggest a robust and reproducible method to measure changes in the shape of white matter fiber bundles. We demonstrate how this method can be used to create a model to assess age-dependent changes in the shape of specific fiber bundles. We derive such models for an ensemble of white matter fiber bundles on our pediatric dataset and show that our results agree with normative human head and brain growth data. Creating these models for a large pediatric longitudinal dataset may improve understanding of both normal development and pathologic states and propose novel parameters for the examination of the pediatric brain.
We describe a fully automatic framework for classification of two types of dementia based on the differences in the shape of brain structures. We consider Alzheimer’s disease (AD), mild cognitive impairment of individuals who converted to AD within 18 months (MCIc), and normal controls (NC). Our approach uses statistical learning and a feature space consisting of projection-based shape descriptors, allowing for canonical representation of brain regions. Our framework automatically identifies the structures most affected by the disease. We evaluate our results by comparing to other methods using a standardized data set of 375 adults available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our framework is sensitive to identifying the onset of Alzheimer’s disease, achieving up to 88.13% accuracy in classifying MCIc versus NC, outperforming previous methods.
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