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In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which...
We systematically evaluate the performance of smoothing on several state-of-the-art sparsity prior CT reconstruction algorithms. State-of-the-art algorithms have been implemented and their performance analyzed with and without applying different smoothing filters. Aiming for successful reconstruction from less number of projections, sparsity prior reconstruction algorithms are found to be useful in...
In this paper, we present an innovative compressive sensing based iterative algorithm for tomographic reconstruction. Back-projection has been customized to make it work even when the projections are not uniformly distributed, and thus ensures a better initial guess to start ART iterations. Contour information of the object has been used efficiently for faster and finer reconstruction. Aiming successful...
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