Jonathan Langdon

COMBAT: mobile-Cloud-based cOmpute/coMmunications infrastructure for BATtlefield applications

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 todays 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-of-the- 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 soldiers 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.

Scholte wave generation during single tracking location shear wave elasticity imaging of engineered tissues.

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.

Single tracking location acoustic radiation force impulse viscoelasticity estimation (STL-VE): A method for measuring tissue viscoelastic parameters

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 with multiple tracking location 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 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.

Real-time single track location ultrasound elasticity imaging using graphic processing units

Ultrasound Shearwave Elasticity Imaging (SWEI) is a set of methods for measuring the elastic properties of biological tissues. Since the elastic properties of tissue are known to change with disease state, the application of these methods to clinical staging of disease is an active area of research. However, the fundamental signal generation and processing techniques required to make these elastic property measurements are still being developed. Our technique, Single Track Location Acoustic Radiation Force Impulse (STL-ARFI) imaging, was developed to address spatial uncertainties in the tracking of the shearwaves. Yet, to apply the method in a clinical setting, real-time processing is required. In this work, we develop a signal processing package that provides real-time STL-ARFI elastic images using Graphic Processing Units (GPUs). This acceleration is achieved by way of a highly optimized Windowed Normalized Cross-Correlation (WNCC) algorithm achieving over 5000 frames per second of displacement estimation.

Compensating for Scholte waves in single track location shearwave elasticity imaging

The estimation of shearwave velocity in biological tissues using Single Track Location Shearwave Elasticity Imaging (STL-SWEI) depends on the assumption that the ultrasonically observed particle displacements are due to the propagation of shearwaves in an approximately infinite space. When this assumption is violated, erroneous estimates of the shearwave speed may occur leading to image artifacts. One particularly troubling error occurs when slowly propagating Scholte waves are generated at solid-fluid interfaces. These interface waves travel at a slower speed than the shearwaves produced in STL-SWEI. However, the signals produced appear similar to that of shearwaves and cannot be readily distinguished in the typical STL-SWEI imaging sequence. Instead, alternative sequences are needed to identify and correct for these anomalous wave types. In this work, the surface wave phenomena is examined in the context of STL-SWEI imaging. The appearance of these waves is demonstrated in simulation, tissue mimicking p...

Single Track Location Viscosity Estimation by maximum likelihood estimation

Single Track Location (STL) Acoustic Radiation Force Impulse (ARFI) imaging has been demonstrated in the setting of linear elastic materials. However, biological tissues are viscoelastic causing shearwave dispersion. As a result, the use of cross-correlation to estimate shearwave speed is not optimal. Specifically, the assumption of shift-invariance is violated. Single Track Location Viscosity Estimation (STL-VE) is an alternative reconstruction that overcomes this limitation by applying a viscoelastic model to the reconstruction. Constraints on the problem such as a limited range and number of tracking locations, and a limited number of time samples make naive frequency domain reconstructions challenging. Instead, we introduce an time domain reconstruction based on the maximum likelihood estimator (MLE). Improved accuracy compared to fitting directly to the signal phase data is demonstrated. The effect of geometry assumptions on the estimation results are explored. Error introduced by spectrum based estimation is demonstrated for trunctated data. Finally, the importance of using a single tracking location is demonstrated.

Minimization of Displacement Estimation Bias Due to High Amplitude-Reflections Using Envelope-Weighted Normalization

In elastography, displacement estimation is often performed using cross-correlation-based techniques, assuming fully-developed, homogeneous speckle. In the presence of a local, large variation in echo amplitude, such as a reflection from a vessel wall, this assumption does not hold true, resulting in a biased displacement estimate. Normalizing the echo by its envelope before displacement estimation reduces this effect at the cost of larger jitter errors. An algorithm is proposed to reduce amplitude-dependent bias in displacement estimates while avoiding a large increase in the jitter error magnitude. The algorithm involves ‘Envelope-Weighted Normalization’ (EWN) of echo data before displacement estimation. A parametric analysis was conducted to find the optimum parameters with which this technique could be implemented. The EWN technique was found to significantly reduce the rms error of the displacement estimates, showing the greatest improvements when utilizing longer window lengths and higher ultrasonic frequencies.

Simulation of shear wave elasticity imaging including speckle and refraction effects

We present an overview of, and results from, a GPU-accelerated, 3D finite-difference time-domain acoustic radiation force impulse (ARFI) shear wave elasticity imaging (SWEI) simulator recently developed in our laboratory. The simulator allows modeling of the ultrasound “push” beam used to generate the shear wave, propagation of the shear wave in a viscoelastic, inhomogeneous medium, and simulation of ultrasound tracking of the shear wave. Spatial variations in both the ultrasound and shear wave speeds can be included to model ultrasound beam refraction errors and shear wave reflection and refraction by inclusions. Speckle has been shown experimentally to induce distortions in the apparent shape of a tracked shear wave, as well as biases in shear wave arrival time estimates used to generate shear wave speed images. A complete simulation of fully developed speckle captures this speckle bias effect but is time consuming. We present a dominant-speckle simulation approach that allows realistic modeling of the ...

On the use of local speckle field as a correction factor for shear modulus estimates based on multiple-track-locations methods

Acoustic-radiation-force impulse (ARFI) imaging for characterization of shear modulus of biological tissues employs either multiple-track-locations (MTL) methods or single-track-location (STL) methods. MTL estimates of shear modulus at different depths suffer from more variability compared to those of STL estimates. Our studies have shown a significant correlation between bias in shear-wave arrival time estimates and local speckle field lateral statistics. We propose using the local speckle field as a surrogate for the unknown bias apparent in MTL shear-wave speed estimates. This local speckle field is determined using a “swept-receive acquisition” that is produced by holding the transmit beam fixed while laterally translating the receive aperture. Application of various lateral weighting functions to the swept-receive image results in an approximate compensation to the tracking bias. In this study, we implement our technique on simulation and experimental data from gelatin phantoms. Additionally, the eff...