Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

ABSTRACT: Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint based on the 3-D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3-D dictionary learning (3-DDL) method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3-DDL method. To justify the proposed method, we first analyze the distributions of the representation coefficients associated with the 3-D dictionary and the conventional 2-D dictionary to compare their efficiencies in

SUBMITTER: Bai T 

PROVIDER: S-EPMC5732496 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature