A spectral interior CT by a framelet-based reconstruction algorithm

Wang, Yingmei; Wang, Ge; Mao, Shuwei; Cong, Wenxiang; Ji, Zhilong; Cai, Jian-Feng; Ye, Yangbo

doi:10.3233/xst-160586

Cited by 3 publications

(5 citation statements)

References 37 publications

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“…In our previous work [14,15], I 0 (E) is constant for all X-ray beams. Here we assume I 0 (E) varies with respect to positions of X-ray beams and take bowtie filtering into consideration.…”

Section: Methodsmentioning

confidence: 93%

“…In this section we extend the framelet-based spectral reconstruction algorithm from fan-beam geometry [14][15][16][17][18] to multi-slice spiral scanning with the bowtie filtering making it more practical in clinical applications.…”

Section: Methodsmentioning

confidence: 99%

“…Besides, for a hospital, adding a new spectral CT scanner to its lab which has facilitated a conventional CT scanner involves practical issues in cost and space. Recently, a framelet-based iterative reconstruction algorithm is proposed to solve the spectral CT reconstruction problem from the aspect of software in the current CT hardware platform [14][15][16][17][18] by our group. The key technique is the sparse representation of X-ray attenuation coefficients in a framelet system.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

Zhang

Mao

et al. 2021

Front. Phys.

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Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.

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“…In our previous work [14,15], I 0 (E) is constant for all X-ray beams. Here we assume I 0 (E) varies with respect to positions of X-ray beams and take bowtie filtering into consideration.…”

Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

Zhang

Mao

et al. 2021

Front. Phys.

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“…In recent years, most of the developed methods for CT image reconstruction are analysis-based frame methods [ 16 , 21 , 26 ], and many state-of-the-art methods such as Split Bregman method [ 27 ], alternative direction [ 28 ], and augmented Lagrangian method [ 29 ] are implemented to solve these problems. The researches of image reconstruction modeled based on balanced method [ 25 ] and synthesis method [ 30 ] are relatively few.…”

Section: The Proposed Model and Methodsmentioning

confidence: 99%

“…For convenience, we first present some basic definitions and some results of the wavelet tight frame used in the proposed model. More details can be seen in [ 20 , 21 ].…”

Section: Preliminariesmentioning

confidence: 99%

Frame-Based CT Image Reconstruction via the Balanced Approach

Zhou

Xiang

2017

Journal of Healthcare Engineering

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Frame-based regularization method as one kind of sparsity representation method has been developed in recent years and has been proved to be an efficient method for CT image reconstruction. However, most of the developed CT image reconstruction methods are analysis-based frame methods. This paper proposes a novel frame-based balanced hybrid model with two sparse regularization terms for CT image reconstruction. We generalize the fast alternating direction method to solve the proposed model so that every subproblem can be easily solved. The numerical experiments suggest that the proposed hybrid balanced-based wavelet regularization scheme is efficient in terms of reducing the defined reconstruction root mean squared error and improving the signal to noise ratio in CT image reconstruction.

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Initial analysis of the middle problem in CT image reconstruction

Yang

Wang

2017

XST

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The interior and exterior problems have been extensively studied in the field of reconstruction of computed tomography (CT) images, which lead to important theoretical and practical results. In this study, we formulate a middle problem of CT image reconstruction, which is more challenging than either the interior or exterior problems. In the middle problem of CT image reconstruction, projection data are measured through and only through the middle dough-like region, so that each projection profile misses data not only internally but also on both sides. For an object with a radially symmetric exterior, we proved that the middle problem could be uniquely solved if the middle ring-shaped zone is piecewise constant or there is a known sub-region inside this middle region. Then, we designed and evaluated a POCS-based algorithm for middle tomography, which is to reconstruct a middle image only from the available data. Finally, the remaining issues are also discussed for further research.

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A spectral interior CT by a framelet-based reconstruction algorithm

Cited by 3 publications

References 37 publications

Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

Frame-Based CT Image Reconstruction via the Balanced Approach

Initial analysis of the middle problem in CT image reconstruction

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