Iterative CT reconstruction from few projections for the nondestructive post irradiation examination of nuclear fuel assemblies

The core components (e.g. fuel assemblies, spacer grids, control rods) of the nuclear reactors encounter harsh environment due to high temperature, physical stress, and a tremendous level of radiation. The integrity of these elements is crucial for safe operation of the nuclear power plants. The Post Irradiation Examination (PIE) can reveal information about the integrity of the elements during normal operations and off‐normal events. Computed tomography (CT) is a tool for evaluating the structural integrity of elements non-destructively. CT requires many projections to be acquired from different view angles after which a mathematical algorithm is adopted for reconstruction. Obtaining many projections is laborious and expensive in nuclear industries. Reconstructions from a small number of projections are explored to achieve faster and cost-efficient PIE. Classical reconstruction algorithms (e.g. filtered back projection) cannot offer stable reconstructions from few projections and create severe streaking artifacts. In this thesis, conventional algorithms are reviewed, and new algorithms are developed for reconstructions of the nuclear fuel assemblies using few projections. CT reconstruction from few projections falls into two categories: the sparse-view CT and the limited-angle CT or tomosynthesis. Iterative reconstruction algorithms are developed for both cases in the field of compressed sensing (CS). The performance of the algorithms is assessed using simulated projections and validated through real projections. The thesis also describes the systematic strategy towards establishing the conditions of reconstructions and finds the optimal imaging parameters for reconstructions of the fuel assemblies from few projections. --Abstract, page iii

Contrast enhancement of digital mammography based on multi-scale analysis

A contrast enhancement algorithm is developed for digital mammograms aiming to assist radiologists in discerning early breast cancer easily. The algorithm is based on a Laplacian pyramid framework image processing technique. The mammogram is decomposed into three frequency sub-bands, low, mid, and high frequency sub-band images. The lower sub-band image contains very fine details and higher level contains coarser features. In this method contrast enhancement is achieved from high and mid sub-bands by decomposing the image based on multi-scale Laplacian pyramid and enhance contrast by image processing. Several mapping functions are applied on sub-band images based on experimental analysis. After modifying sub-band images using mapping functions, the final image is derived from reconstruction of the Laplacian images from lower resolution level to upper resolution level. To demonstrate the effectiveness of the algorithm, two mammogram images are analyzed. To validate the algorithm, quantitative measurements are performed. Several existing contrast enhancement techniques are compared with the developed algorithm. Experimental results and quantitative evaluation prove that the proposed algorithm offers improved contrast of digital mammograms --Abstract, page iv