23,903 research outputs found
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180 view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180 measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively.Comment: Spotlight presentation at CVPR 201
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
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