Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.
Quantitative imaging in medicine and surgery(2023)
Abstract
We developed a method in which the global, local, and nonlocal priors are jointly used to develop the reconstruction model for low-dose MECT, where the global low-rankness and nonlocal prior are cascaded by subspace decomposition and block-matching, and the L0 sparsity is applied to express the local prior. The results of the experiments demonstrate that the proposed method based on subspace improves computational efficiency and has advantages in noise suppression and structure preservation over competing algorithms.
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Key words
Multienergy computed tomography (MECT),global priors,image reconstruction,local and nonlocal priors,subspace decomposition
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