Swinging multi-source industrial CT systems for aperiodic dynamic imaging

Wu, Weiwen; Yu, Hengyong; Gong, Changcheng; Liu, Fenglin

doi:10.1364/oe.25.024215

Cited by 24 publications

(14 citation statements)

References 33 publications

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“…3, 1.2% iodine is injected into the mouse thorax phantom as contrast agent. A polychromatic 50KVp x-ray source is assumed, and it is divided into eight different energy channels: [16,22) keV, [22,25) keV, [25,28) keV, [28,31) keV, [31,34) keV, [34,37) keV, [37,41) keV, and [41,50) keV. The PCD used in the experiment contains 512 detector elements, and each element covers a length of 0.1mm.…”

Section: Mouse Thorax Phantom Studymentioning

confidence: 99%

Spatial-spectral cube matching frame for spectral CT reconstruction

Zhang

Wang

et al. 2018

Inverse Problems

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Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts: i) human body usually consists of two or three basic materials implying that the reconstructed spectral images have a strong sparsity; ii) the same basic material component in a single channel image has similar intensity and structures in local regions. Different material components within the same energy channel share similar structural information; iii) multi-energy projection datasets are collected from the subject by using different narrow energy windows, which means images reconstructed from different energy-channels share similar structures. To explore those information, we first establish a tensor cube matching frame (CMF) for a BM4D denoising procedure. Then, as a new regularizer, the CMF is introduced into a basic spectral CT reconstruction model, generating the SSCMF method. Because the SSCMF model contains an L 0-norm minimization of 4D transform coefficients, an effective strategy is employed for optimization. Both numerical simulations and realistic preclinical mouse studies are performed. The results show that the SSCMF method outperforms the state-of-the-art algorithms, including the simultaneous algebraic reconstruction technique, total variation minimization, total variation plus low rank, and tensor dictionary learning.

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Section: Mouse Thorax Phantom Studymentioning

confidence: 99%

Spatial-spectral cube matching frame for spectral CT reconstruction

Zhang

Wang

et al. 2018

Inverse Problems

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“…X -RAY computed tomography (CT), as one of advanced nondestructive imaging tools, can reconstruct the internal tomography by collecting the measurements from 360 or 180 + ϕ (ϕ is fan-beam angle) for fan-beam geometry [1]- [3]. However, due to the restriction of the scanning environment in some applications, it is difficult to obtain the sufficient projections for image reconstruction and then further results in limited-angle CT reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning

Luo

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Limited-angle X-ray computed tomography (CT) reconstruction is a typical ill-posed problem. To recover satisfied reconstructed images with limited-angle CT projections, prior information is usually introduced into image reconstruction, such as the piece-wise constant, nonlocal image similarity, and so on. To further improve the image quality for limited-angle CT reconstruction, the dictionary learning (DL) and image gradient 0-norm are combined into image reconstruction model, it can be called as 0 DL reconstruction technique. The advantages of 0 DL can be divided into two aspects. On one hand, the proposed 0 DL method can inherit the advantages of DL in image details preservation and features recovery by exploring an over-complete dictionary. On the other hand, the image gradient 0-norm minimization can further protect image edges and reduce shadow artifact. Both numerical simulation and preclinical mouse experiments are performed to validate and evaluate the outperformances of proposed 0 DL method by comparing with other state-of-the-art methods, such as total variation (TV) minimization and TV with low rank (TV + LR). Index Terms-Dictionary learning (DL), image reconstruction, 0-norm of image gradient, limited-angle X-ray CT.

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“…As an imaging tool, x-ray computed tomography (CT) has been widely applied in clinical diagnosis, industrial detection and security inspections [1,2]. However, there are some inherent weaknesses in the conventional CT. First，the traditional CT images have no sufficient tissue contrast resolution and material decomposition analysis capability [3].…”

Section: Introductionmentioning

confidence: 99%

Low-dose spectral CT reconstruction using image gradient ℓ0–norm and tensor dictionary

Zhang

Wang

et al. 2018

Applied Mathematical Modelling

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143

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Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient ℓ0-norm, which is named as ℓ0TDL. The ℓ0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the ℓ0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The resultsshow that the proposed ℓ0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

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Swinging multi-source industrial CT systems for aperiodic dynamic imaging

Cited by 24 publications

References 33 publications

Spatial-spectral cube matching frame for spectral CT reconstruction

Spatial-spectral cube matching frame for spectral CT reconstruction

Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning

Low-dose spectral CT reconstruction using image gradient ℓ0–norm and tensor dictionary

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