Compressed sensing inspired rapid algebraic reconstruction technique for computed tomography

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 reconstruction with minimum number of iterations, conjugate gradient method that enjoys the full benefit of ART with good initial guess has been used instead of commonly used steepest descent method. Based on the experiments on simulated and real medical images it has been shown that the proposed modality is capable of producing much better reconstruction than the state-of-the-art methods.

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