Tomographic breast imaging techniques can potentially improve detection and diagnosis of cancer in women with radiodense and/or fibrocystic breasts. We have developed a high-resolution positron emission mammography/tomography imaging and biopsy device (called PEM/PET) to detect and guide the biopsy of suspicious breast lesions. PET images are acquired to detect suspicious focal uptake of the radiotracer and guide biopsy of the area. Limited-angle PEM images could then be used to verify the biopsy needle position prior to tissue sampling. The PEM/PET scanner consists of two sets of rotating planar detector heads. Each detector consists of a 4 x 3 array of Hamamatsu H8500 flat panel position sensitive photomultipliers (PSPMTs) coupled to a 96 x 72 array of 2 x 2 x 15 mm(3) LYSO detector elements (pitch = 2.1 mm). Image reconstruction is performed with a three-dimensional, ordered set expectation maximization (OSEM) algorithm parallelized to run on a multi-processor computer system. The reconstructed field of view (FOV) is 15 x 15 x 15 cm(3). Initial phantom-based testing of the device is focusing upon its PET imaging capabilities. Specifically, spatial resolution and detection sensitivity were assessed. The results from these measurements yielded a spatial resolution at the center of the FOV of 2.01 +/- 0.09 mm (radial), 2.04 +/- 0.08 mm (tangential) and 1.84 +/- 0.07 mm (axial). At a radius of 7 cm from the center of the scanner, the results were 2.11 +/- 0.08 mm (radial), 2.16 +/- 0.07 mm (tangential) and 1.87 +/- 0.08 mm (axial). Maximum system detection sensitivity of the scanner is 488.9 kcps microCi(-1) ml(-1) (6.88%). These promising findings indicate that PEM/PET may be an effective system for the detection and diagnosis of breast cancer.

No abstract

During the past several years, the authors have conducted experiments in the use of writing to learn (WTL) techniques in sophomore-level engineering mechanics courses at West Virginia University (WVU). The work was a collaborative effort between a doctoral candidate in the Curriculum and Instruction Department and an Associate Professor of Mechanical and Aerospace Engineering. This paper focuses on the effectiveness of several writing to learn strategies, assigned and completed in the Engineer's Log, which students identified as successful methods for learning. We present and discuss these strategies using sample log entries to illustrate their use and to suggest that expressive writing, or writing for the self, continues to prove itself a successful technique for learning. Finally, we evaluate the data provided and discuss our results.

In this paper, an approach to predicting randomly-shaped particle volume based on its two-Dimensional (2-D) digital image is explored. Conversion of gray-scale image of the particles to its binary counterpart is first performed using backlighting technique. The silhouette of particle is thus obtained, and consequently, informative features such as particle area, centroid and shape-related descriptors are collected. Several dimensionless parameters are defined, and used as regressor variables in a multiple linear regression model to predict particle volume. Regressor coefficients are found by fitting to a randomly selected sample of 501 particles ranging in size from 4.75mm to 25mm. The model testing experiment is conducted against a different aggregate sample of the similar statistical properties, the errors of the model-predicted volume of the batch is within ±2%.

This paper presents the study of the effect variations in the heat effluence from a solid oxide fuel cell (SOFC) has on a gas turbine hybrid configuration. The SOFC is simulated through hardware at the U.S. Department of Energy, National Energy Technology Laboratory (NETL). The gas turbine, compressor, recuperative heat exchanger, and other balance of plant components are represented by actual hardware in the Hybrid Performance Test Facility at NETL. Fuel cell heat exhaust is represented by a combustor that is activated by a fuel cell model that computes energy release for various sensed system states System structure is derived by means of frequency response data generated by the sinusoidal oscillation of the combustor fuel valve over a range of frequencies covering three orders of magnitude. System delay and order are obtained from Bode plots of the magnitude and phase relationships between input and output parameters. Transfer functions for mass flow, temperature, pressure, and other states of interest are derived as a function of fuel valve flow, representative of fuel cell thermal effluent. The Bode plots can validate existing analytical transfer functions, provide steady state error detection, give a stability margin criterion for the fuel valve input, estimate system bandwidth, identify any nonminimum phase system behavior, pinpoint unstable frequencies, and serve as an element of a piecewise transfer function in the development of an overall transfer function matrix covering all system inputs and outputs of interest. Further loop shaping techniques and state space representation can be applied to this matrix in a multivariate control algorithm.