Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms

Emil Y. Sidky; Jakob H. Jørgensen; Xiaochuan Pan

doi:10.1117/12.913307

3 March 2012 Sampling conditions for gradient-magnitude sparsity based image reconstruction algorithms

Emil Y. Sidky, Jakob H. Jørgensen, Xiaochuan Pan

Proceedings Volume 8313, Medical Imaging 2012: Physics of Medical Imaging; 831337 (2012) https://doi.org/10.1117/12.913307
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

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.

Citation Download Citation

Emil Y. Sidky, Jakob H. Jørgensen, and 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); https://doi.org/10.1117/12.913307

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