Dataset Information

Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.

ABSTRACT:

Background

Multienergy computed tomography (MECT) is a promising imaging modality for material decomposition, lesion detection, and other clinical applications. However, there is an urgent need to design efficient and accurate algorithms to solve the inverse problems related to spectral reconstruction and improve image quality, especially under low-dose and incomplete datasets. The key issue for MECT reconstruction is how to efficiently describe the interchannel and intrachannel priors in multichannel images.

Methods

In this model, in order to correlate the similarities of interchannel images and regularize the multichannel images, the global, local, and nonlocal priors are jointly integrated into the low-dose MECT reconstruction model. First, the subspace decomposition method first employs the global low-rankness to map the original MECT images to the low-dimensional eigenimages. Then, nonlocal self-similarity of the eigenimages is cascaded into the optimization model. Additionally, the L0 quasi-norm on gradient images is incorporated into the proposed method to further enhance the local sparsity of intrachannel images. The alternating direction method is applied to solve the optimization model in an iterative scheme.

Results

Simulation, preclinical, and real datasets were applied to validate the effectiveness of the proposed method. From the simulation dataset, the new method was found to reduce the root-mean-square error (RMSE) by 42.31% compared with the latest research fourth-order nonlocal tensor decomposition MECT reconstruction (FONT-SIR) method under 160 projection views. The calculation time of an iteration for the proposed method was 23.07% of the FONT-SIR method. The results of material decomposition in real mouse data further confirmed the accuracy of the proposed method for different materials.

Conclusions

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.

SUBMITTER: Yu X

PROVIDER: S-EPMC9929412 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.

Yu Xiaohuan X Cai Ailong A Li Lei L Jiao Zhiyong Z Yan Bin B

Quantitative imaging in medicine and surgery 20230105 2

<h4>Background</h4>Multienergy computed tomography (MECT) is a promising imaging modality for material decomposition, lesion detection, and other clinical applications. However, there is an urgent need to design efficient and accurate algorithms to solve the inverse problems related to spectral reconstruction and improve image quality, especially under low-dose and incomplete datasets. The key issue for MECT reconstruction is how to efficiently describe the interchannel and intrachannel priors i ...[more]

PMID: 36819241

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Project description:BackgroundMulti-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension.MethodsIn this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity.ResultsNumerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s.ConclusionsTo further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

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Dataset Information

Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.

Background

Methods

Results

Conclusions

Publications

Low-dose spectral reconstruction with global, local, and nonlocal priors based on subspace decomposition.

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