3 March 2012 Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms
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Image reconstruction from sparse-view data in 2D fan-beam CT is investigated by constrained, total-variation minimization. This optimization problem exploits possible sparsity in the gradient magnitude image (GMI). The investigation is performed in simulation under ideal, noiseless data conditions in order to reveal a possible link between GMI sparsity and the necessary number of projection views for reconstructing an accurate image. Results are shown for two, quite different phantoms of similar GMI sparsity.
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Emil Y. Sidky, Emil Y. Sidky, Jakob H. Jørgensen, Jakob H. Jørgensen, Xiaochuan Pan, Xiaochuan Pan, } "Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831337 (3 March 2012); doi: 10.1117/12.913307; https://doi.org/10.1117/12.913307

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