The Mixed Waste Focus Area (MWFA) Characterization Development Strategy delineates an approach to resolve technology deficiencies associated with the characterization of mixed wastes. The intent of this strategy is to ensure the availability of technologies to support the Department of Energy� s (DOE) mixed waste low-level or transuranic (TRU) contaminated waste characterization management needs. To this end the MWFA has defined and coordinated characterization development programs to ensure that data and test results necessary to evaluate the utility of non-destructive assay technologies are available to meet site contact handled waste management schedules. Requirements used as technology development project benchmarks are based in the National TRU Program Quality Assurance Program Plan. These requirements include the ability to determine total bias and total measurement uncertainty. These parameters must be completely evaluated for waste types to be processed through a given nondestructive waste assay system constituting the foundation of activities undertaken in technology development projects. Once development and testing activities have been completed, Innovative Technology Summary Reports are generated to provide results and conclusions to support EM-30, -40, or -60 end user/customer technology selection. The Active and Passive Computed Tomography non-destructive assay system is one of the technologies selected for development by the MWFA. Lawrence Livermore National Laboratory� s (LLNL) is developing the Active and Passive Computed Tomography (AP low level, transuranic or provide results and conclusions to support EM-30, -40, or -60 end user/customer technology selection. The Active and Passive Computed Tomography non-destructive assay system is one of the technologies selected for development by the MWFA. Lawrence Livermore National Laboratory� s (LLNL) is developing the Active and Passive Computed Tomography (AP low level, transuranic or mixed, which contains radioactivity and hazardous organic species. The scope of our technology is to develop a non-invasive waste-drum scanner that employs the principles of computed tomography and gamma-ray spectral analysis to identify and quantify all of the detectable radioisotopes. Once this and other applicable technologies are developed, waste drums can be non- destructively and accurately characterized to satisfy repository and regulatory guidelines prior to disposal.

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Active and passive computed tomography mixed waste focus area final report

The goal of this work is to demonstrate the feasibility of 3D imaging of coronary stents using fluoroscopy. This technique could potentially provide an inexpensive and non- invasive alternative to stent inspection by intravascular ultrasound. The major difficulty to e overcome is real or apparent motion of the stent between successive views. We solve this problem by tracking a feature point ont he stent prior to performing the 3D reconstruction. We shifted the images to eliminate this apparent motion, then reconstructed the 3D stent image using iterative backprojection. The stent cross-sectional images are successfully reconstructed with spatial resolution of approximately 0.4 mm. This successful reconstruction of a coronary stent in vitro demonstrates the feasibility of 3D imaging of coronary stents using fluoroscopy.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Fluoroscopic 3D reconstruction of coronary stents

The quality of images reconstructed from projections obtained by transmission tomography depends on the range of angles over which measurements can be made as well as the number of projections. Conventional methods such as filtered backprojection suffer when the number of measurements is small, and methods such as ART produce noticeable artifacts when the angular range is limited. Another possible approach is the direct minimization of the squared error between the measurements and the projection of the reconstructed image onto the measurement space. Alternatively, the unfiltered backprojection of the data can be modeled as a linear blur of the desired image, and this blur can be removed with a deconvolution algorithm. One way to handle the latter approach is to minimize the squared error between the backprojection and the reconstructed image blurred by an appropriately chosen point spread function. These methods result in higher quality images when the angular range is limited and the number of projections is small. We use a conjugate gradient based constrained optimization algorithm to do the minimization. The available constraints on the variables are upper and lower bounds and a hyperplane constraint. Since the variables in this case are the image pixels, we can enforce known bounds on the pixel values, such as nonnegativity, as well as keep the sum of the pixels at its known value. These constraints greatly improve the reconstruction quality and increase the rate of convergence of the algorithm.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Application of a Constrained Optimization Algorithm to Limited View Tomography

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Application of a Constrained Optimization Algorithm to Limited View Tomography