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

Wu, Weiwen; Zhang, Yanbo; Wang, Qian; Liu, Fenglin; Chen, Peijun; Yu, Hengyong

doi:10.1016/j.apm.2018.07.006

Cited by 135 publications

(76 citation statements)

References 44 publications

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“…In recent years, researchers are becoming increasingly interested in regularized iterative reconstruction algorithms for incomplete projection data, as these algorithms can add some prior knowledge to obtain better reconstructed image and will not be affected by the geometrical structure of the scanning mode. Hence, more and more researchers are keen to construct an appropriate transformation that can utilize prior information of the reconstructed object, and various regularized iterative reconstruction algorithms have been proposed [6][7][8][9][10]. As one of the regularized iterative reconstruction algorithms, total variation (TV)-based minimization method [11] can suppress the streak artifacts and noise when the projection data are acquired within a few-views scanning mode.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

et al. 2020

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As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures. OPEN ACCESS Citation: Wang J, Liang J, Cheng J, Guo Y, Zeng L (2020) Deep learning based image reconstruction algorithm for limited-angle translational computed tomography. PLoS ONE 15(1): e0226963. https://

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Section: Introductionmentioning

confidence: 99%

Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

et al. 2020

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“…In theoretical, the PCD can effectively reduce the electronic noise in acquired data and suppress the data noise by counting each entering photon of the detector [13]. It can also distinguish the energy of incident photons to obtain more Xray energy information and a higher discrimination degree than existing DECT, which can effectively control noise amplification in the reconstruction process or other jobs [14]. In fact, because of the influence of X-ray fluorescence, charge sharing, K-escape, and pulse pileups, the accuracy of the material decomposition is decreased.…”

Section: Introductionmentioning

confidence: 99%

Image-Domain Based Material Decomposition by Multi-Constraint Optimization for Spectral CT

Feng

Wang

et al. 2020

IEEE Access

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As a new generation computed tomography (CT) technology, spectral CT has great potential in many aspects, especially in the identification and decomposition of materials. To achieve higher accuracy of materials decomposition, we propose a multi-constraint based nonlocal total variation (NLTV) method, named as MCNLTV. Because image-domain based material decomposition belongs to the twostep material decomposition method, the Filter Back-Projection (FBP) algorithm or SART algorithm is used to reconstruct spectral CT images in the first step. Then the material attenuation coefficient matrix is obtained from the reconstruction results. In the second step, MCNLTV regularization is utilized to obtain the material decomposition image. Both simulation experiments and real data experiments are carried out. Experiment results show that the proposed method can obtain higher accuracy of material decomposition than traditional total variation based material decomposition (TVMD), ROF-LLT regularization and direct inverse transformation (DI) for spectral CT. INDEX TERMS Spectral CT; image-domain; multi-constraint optimization; material decomposition.

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“…A tensor dictionary learning (TDL) was introduced to explore the image similarity among different energy bins [25]. Considering the similarity between the image gradient of different energy bins, the image gradient L0-norm was incorporated into the TDL (L0TDL) framework for sparse-view spectral CT reconstruction [26]. The spectral prior image constrained compressed sensing algorithm (spectral PICCS) [27], TV-TV and total variation spectral mean (TV-SM) methods [28] can also be considered as prior-image-knowledge based methods, where a high quality image is treated as prior to constrain the final solution [29].…”

Section: Introductionmentioning

confidence: 99%

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Liu

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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Spectralcomputed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise-ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes. The small size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost and memory load might not be affordable in practice. To avoid the above limitations and further improve image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a new regularizer, the Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms other state-ofthe-art competing algorithms.

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

Cited by 135 publications

References 44 publications

Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

Image-Domain Based Material Decomposition by Multi-Constraint Optimization for Spectral CT

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

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