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L0 constrained sparse reconstruction for multi-slice helical CT reconstruction.

Abstract : In this paper, we present a Bayesian maximum a posteriori method for multi-slice helical CT reconstruction based on an L0-norm prior. It makes use of a very low number of projections. A set of surrogate potential functions is used to successively approximate the L0-norm function while generating the prior and to accelerate the convergence speed. Simulation results show that the proposed method provides high quality reconstructions with highly sparse sampled noise-free projections. In the presence of noise, the reconstruction quality is still significantly better than the reconstructions obtained with L1-norm or L2-norm priors.
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https://www.hal.inserm.fr/inserm-00566886
Contributor : Lotfi Senhadji <>
Submitted on : Thursday, February 17, 2011 - 6:37:08 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Long-term archiving on: : Wednesday, May 18, 2011 - 2:48:13 AM

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Yining Hu, Lizhe Xie, Limin Luo, Jean Claude Nunes, Christine Toumoulin. L0 constrained sparse reconstruction for multi-slice helical CT reconstruction.. Physics in Medicine and Biology, IOP Publishing, 2011, 56 (4), pp.1173-89. ⟨10.1088/0031-9155/56/4/018⟩. ⟨inserm-00566886⟩

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