CT Image Reconstruction via Nonlocal Low-Rank Regularization and Data-Driven Tight Frame

X-ray computed tomography (CT) is widely used in medical applications, where many efforts have been made for decades to eliminate artifacts caused by incomplete projection. In this paper, we propose a new CT image reconstruction model based on nonlocal low-rank regularity and data-driven tight frame (NLR-DDTF). Unlike the Spatial-Radon domain data-driven tight frame regularization, the proposed NLR-DDTF model uses an asymmetric treatment for image reconstruction and Radon domain inpainting, which combines the nonlocal low-rank approximation method for spatial domain CT image reconstruction and data-driven tight frame-based regularization for Radon domain image inpainting. An alternative direction minimization algorithm is designed to solve the proposed model. Several numerical experiments and comparisons are provided to illustrate the superior performance of the NLR-DDTF method.

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CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization

SIAM Journal on Imaging Sciences ◽

10.1137/16m105928x ◽

2016 ◽

Vol 9 (3) ◽

pp. 1063-1083 ◽

Cited By ~ 13

Author(s):

Ruohan Zhan ◽

Bin Dong

Keyword(s):

Image Reconstruction ◽

Tight Frame ◽

Data Driven ◽

Ct Image ◽

Ct Image Reconstruction

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Data-Driven Adversarial Learning for Sinogram-Based Iterative Low-Dose CT Image Reconstruction

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) ◽

10.1109/icstcc.2019.8885947 ◽

2019 ◽

Author(s):

Anamaria Vizitiu ◽

Andrei Puiu ◽

Sureerat Reaungamornrat ◽

Lucian Mihai Itu

Keyword(s):

Image Reconstruction ◽

Low Dose ◽

Data Driven ◽

Ct Image ◽

Adversarial Learning ◽

Low Dose Ct ◽

Ct Image Reconstruction

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Undersampled MR Image Reconstruction with Data-Driven Tight Frame

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/424087 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Jianbo Liu ◽

Shanshan Wang ◽

Xi Peng ◽

Dong Liang

Keyword(s):

Image Reconstruction ◽

Tight Frame ◽

Data Driven ◽

Iteration Algorithm ◽

Mr Image ◽

Magnetic Image ◽

Structure Information ◽

Reconstruction Methods ◽

Proposed Model ◽

Efficiency And Effectiveness

Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

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