Single Tracking Location (STL) Shear wave Elasticity Imaging (SWEI) is a method for detecting elastic differences between tissues. It has the advantage of intrinsic speckle bias suppression compared to Multiple Tracking Location (MTL) variants of SWEI. However, the assumption of a linear model leads to an overestimation of the shear modulus in viscoelastic media. A new reconstruction technique denoted Single Tracking Location Viscosity Estimation (STL-VE) is introduced to correct for this overestimation. This technique utilizes the same raw data generated in STL-SWEI imaging. Here, the STL-VE technique is developed by way of a Maximum Likelihood Estimation (MLE) for general viscoelastic materials. The method is then implemented for the particular case of the Kelvin-Voigt Model. Using simulation data, the STL-VE technique is demonstrated and the performance of the estimator is characterized. Finally, the STL-VE method is used to estimate the viscoelastic parameters of ex-vivo bovine liver. We find good agreement between the STL-VE results and the simulation parameters as well as between the liver shear wave data and the modeled data fit.
The amount of data processed annually over the Internet has crossed the zetabyte boundary, yet this Big Data cannot be efficiently processed or stored using today's mobile devices. Parallel to this explosive growth in data, a substantial increase in mobile compute-capability and the advances in cloud computing have brought the state-ofthe-art in mobile-cloud computing to an inflection point, where the right architecture may allow mobile devices to run applications utilizing Big Data and intensive computing. In this paper, we propose the MObile Cloud-based Hybrid Architecture (MOCHA), which formulates a solution to permit mobile-cloud computing applications such as object recognition in the battlefield by introducing a mid-stage compute-and storage-layer, called the cloudlet. MOCHA is built on the key observation that many mobile-cloud applications have the following characteristics: 1) they are compute-intensive, requiring the compute-power of a supercomputer, and 2) they use Big Data, requiring a communications link to cloud-based database sources in near-real-time. In this paper, we describe the operation of MOCHA in battlefield applications, by formulating the aforementioned mobile and cloudlet to be housed within a soldier's vest and inside a military vehicle, respectively, and enabling access to the cloud through high latency satellite links. We provide simulations using the traditional mobile-cloud approach as well as utilizing MOCHA with a mid-stage cloudlet to quantify the utility of this architecture. We show that the MOCHA platform for mobile-cloud computing promises a future for critical battlefield applications that access Big Data, which is currently not possible using existing technology.
The physical environment of engineered tissues can influence cellular functions that are important for tissue regeneration. Thus, there is a critical need for noninvasive technologies capable of monitoring mechanical properties of engineered tissues during fabrication and development. This work investigates the feasibility of using single tracking location shear wave elasticity imaging (STL-SWEI) for quantifying the shear moduli of tissue-mimicking phantoms and engineered tissues in tissue engineering environments. Scholte surface waves were observed when STL-SWEI was performed through a fluid standoff, and confounded shear moduli estimates leading to an underestimation of moduli in regions near the fluid-tissue interface.
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