Locally linear constraint based optimization model for material decomposition

Wang, Qian; Zhu, Yining; Yu, Hengyong

doi:10.1088/1361-6560/aa8e13

Cited by 27 publications

(23 citation statements)

References 54 publications

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“…However, it is difficult to get the X-ray transmission spectrum model in practice. The image-domain based methods [19,20] firstly reconstruct images from the spectral CT dataset and then decompose the materials from the reconstructed results. There are many iterative reconstruction methods developed for the first step, but less work concerned about the second step, such as TDL [21], L0TDL [22], SSCMF [23], NLCTF [24], ASSIST [25], L0-PICCS [26], SISTER [27].…”

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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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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“…These approaches obtain sat-isfying results in varying degrees for removing artifacts and suppressing noise. However, it still remains two main barriers for applying the approach to practical application: the great computation cost caused by the iterative manner of the solving algorithm [25] and the difficulty of choosing the regularization parameters. Hence, it would be desperately desire for an approach with low complicated of computation and convenient parameter choosing in practical application.…”

Section: Introductionmentioning

confidence: 99%

Learning Image From Projection: A Full-Automatic Reconstruction (FAR) Net for Computed Tomography

Zhu

Zhao

2020

IEEE Access

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The x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. The aim of CT is to recover or reconstruct image from projection data. However, in particular, the reconstructed image usually suffers from complex artifacts and noise, such as the sampling is insufficient or low-dose CT. In order to deal with such issues and achieve reconstruction, a full automatic reconstruction (FAR) net is proposed for CT reconstruction via deep learning technique. Different with the usual network in deep learning reconstruction, the proposed neural network is an end-to-end network by which the image is predicted directly from projection data. The main challenge for such a FAR-net is the space complexity of the CT reconstruction in full-connected (FC) network. For a CT image with the size N × N , a typical requirement of memory space for the image reconstruction is O(N 4), for which is unacceptable by conventional calculation device, e.g. GPU workstation. In this paper, we utilize a series of smaller fully connected layers (FCL) to replace the huge Radon transform matrix based on the sparse nonnegative matrix factorization (SNMF) theory. By applying such an approach, the FAR-net is able to reconstruct images with the size 512×512 on only single workstation. The results of numerical experiments show that the projection matrix and the FAR-net is able to reconstruct the CT image from projection data with a superior quality to conventional methods such as optimization based approach. Meanwhile, the factorization for the inverse projection matrix is validated in simulation and real experiments.

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“…12 Under the integral mode, different materials may reveal the same CT value, which infers weak material discrimination. 13 Spectral CT is proposed by extending the conventional CT along the energy dimension. Two or more measurements with different spectral information 14,15 make it possible to split the linear attenuation coefficient into two multiplicative components with energy dependence and independence, respectively.…”

Section: Introductionmentioning

confidence: 99%

Locally linear transform based three‐dimensional gradient ‐norm minimization for spectral CT reconstruction

Wang

Deng

et al. 2020

Medical Physics

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Purpose: Spectral computed tomography (CT) is proposed by extending the conventional CT along the energy dimension. One newly implementation is to employ an energy-discriminating photon counting detector (PCD), which can distinguish photon energy and divide a whole x-ray spectrum into several energy bins with appropriate post-processing steps. The state-of-the-art PCD-based spectral CT has superior energy resolution and material distinguishability, and it further has a great potential in both medical and industrial applications. To improve the reconstruction quality and decomposition accuracy, in this work, we propose an optimization-based spectral CT reconstruction method with an innovational sparsity constraint. Methods: We first employ a locally linear transform to the reconstructed channel images, and the structural similarity along the spectral dimension is effectively converted to a one-dimensional (1D) gradient sparsity. Then, combining the prior knowledge of piecewise constant in the spatial domain (e.g., a two-dimensional (2D) gradient sparsity feature), we unify both spectral and spatial dimensions and establish a joint three-dimensional (3D) gradient sparsity. In addition, we use the L 0-norm to measure the proposed sparsity and incorporate it as a smoothness constraint to concretize a general optimization framework. Furthermore, we develop the corresponding iterative algorithm to solve the optimization problem. Results: Both visual results and quantitative indexes of numerical simulations and phantom experiments demonstrate the proposed method outperform the conventional filtered backprojection (FBP), total variation (TV), 2D L 0-norm (L 0), and TV with low rank (TVLR)-based methods. From the image and ROI comparisons, we find the proposed method performs well in noise suppression, detail maintenance, and decomposition accuracy. However, the FBP suffers severe noise, the TV and L 0 are difficult to work consistently among different energy bins, and the TVLR fails to avoid gray value shift. The image quality assessments, such as peak signal-to-noise ratio (PSNR), normal mean absolute deviation (NMAD). and structural similarity (SSIM), also consistently indicate the proposed method can effectively removing noise and keeping fine structures in both channel-wise reconstructions and material decompositions. Conclusions: By employing a locally linear transform, the structural similarity among spectral channel images is converted to a 1D gradient sparsity and the gray value shift is effectively avoided when the difference measurement is minimized. The 3D L 0-norm jointly and uniformly measures the gradient sparsity in both spectral and spatial dimensions. The cooperation of locally linear transform and 3D L 0-norm well reinforces the global sparse features and keeps the correlation along spectral dimension without bringing gray-value distortions. The corresponding constraint optimization model is fast and stably solved by using an alternative direction technique. Both numerical simulations and phantom experi...

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Locally linear constraint based optimization model for material decomposition

Cited by 27 publications

References 54 publications

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

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

Learning Image From Projection: A Full-Automatic Reconstruction (FAR) Net for Computed Tomography

Locally linear transform based three‐dimensional gradient ‐norm minimization for spectral CT reconstruction

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