Spectral CT Reconstruction Based on PICCS and Dictionary Learning

Kong, Huihua; Lei, Xiaoxue; Lei, Lei; Zhang, Yanbo; Yu, Hengyong

doi:10.1109/access.2020.3010228

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Chen et al proposed a sparse-view CT reconstruction method based on PICCS, which introduced a prior image to accurately reconstruct dynamic CT images from under-sampled projection datasets, and achieved good results [33,34]. Later, the methods based on PICCS were widely used in energy spectrum CT reconstruction, which greatly improved the quality of energy spectrum CT reconstruction [35][36][37][38]. However, most PICCS-based methods are based on TV sparse framework, and the gradient L 1 -norm may cause blurred edges and loss image details.…”

Section: Introductionmentioning

confidence: 99%

Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

Li,

Sun,

2023

Applied Sciences

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The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L1-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L0-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery.

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Section: Introductionmentioning

confidence: 99%

Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

Li,

Sun,

2023

Applied Sciences

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“…Sparse representation and dictionary learning (DL) have achieved great success in the medical imaging community, such as in low-dose image reconstruction [ 19 , 20 ], limited-angle CT reconstruction [ 21 ], spectral CT [ 22 ], etc. The extensive experimental results have shown that the DL-based regularization term is superior to the TV-based candidate in terms of preserving image details and removing artifacts.…”

Section: Introductionmentioning

confidence: 99%

Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support

Mou

2022

Tomography

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Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason–Ludwig consistency condition. Then, the L1norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach.

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“…CT was introduced as an alternative tool designed to help physicians in the examination of multiple diseases. Those tools utilize analysis of the data to evaluate the condition of the patient [1]. Nevertheless, by its characteristics, exposure to high X-ray radiations of CT scans and prolonged acquisition time is yet a bottleneck that can lead to patient discomfort and lifetime risk of cancer [2].…”

Section: Introductionmentioning

confidence: 99%

Total Variant Based Average Sparsity Model With Reweighted Analysis for Compressive Sensing of Computed Tomography

et al. 2021

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Computed tomography (CT) in medical is an imaging procedure employed to generate detailed images of bones, soft tissue, internal organs, and blood vessels. However, prolonged acquisition time is yet a bottleneck that can lead to patient discomfort in addition to the cost constrain and exposure to X-rays used by CT. In medical imaging technologies and implementations, effective sampling and transmission techniques are some of the main areas of study to overcome such problems. To fulfill this requirement, the compressive sensing (CS) technique was introduced demonstrating that such compression is possible and can be accomplished throughout the process of data restoration; and that the uncompressed frames can be recovered employing a scalable approach of computational optimization. Sparsity averaging reweighted analysis (SARA) was proposed in compressed imaging, exploiting multi-basis sparsity with averaging approach and basis pursuit denoise (BPDN) with high signal to noise (SNR) results. In SARA, the processing time is not considered due to the high processing time because of iteration in the reweighted process and it is not feasible for the medical image that needs fast processing with high SNR result. To fulfill this gap, this paper proposes total variation based average sparsity model with reweighted analysis for CT imaging. The SNR, structural similarity index (SSIM), and processing time are used as performance metrics for the comparison of the proposed and existing techniques. From detailed experimental results, the proposed technique outperforms the existing CS techniques and is considered as a feasible solution for compressed sensing (CS) based CT images compression with faster delay process and better visual quality for medical images.INDEX TERMS Average sparsity model, compressive sensing, CT images, SARA, SNR, SSIM, total variation (TV).

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Spectral CT Reconstruction Based on PICCS and Dictionary Learning

Cited by 10 publications

References 29 publications

Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

Sparse-View Computed Tomography Reconstruction Based on a Novel Improved Prior Image Constrained Compressed Sensing Algorithm

Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support

Total Variant Based Average Sparsity Model With Reweighted Analysis for Compressive Sensing of Computed Tomography

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