Tomography Reconstructions With Stochastic Level
In this work, we propose a stochastic level-set method to reconstruct binary tomography cross-sections from few projections. A first reconstruction image is obtained with a level-set regularization method. The reconstruction is then refined with a stochastic partial differential equation based on a Stratanovitch formulation.
The reconstruction results are compared with the ones obtained with the classical simulated annealing method. The methods are tested on a complex bone μ- CT cross-section for different noise levels and number of projections. The best reconstruction results are obtained with the stochastic level set-method.
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