Image‐domain multimaterial decomposition for dual‐energy CT based on prior information of material images

Ding, Qiaoqiao; Niu, Tianye; Zhang, Xiaoqun; Long, Yong

doi:10.1002/mp.13001

Cited by 28 publications

(27 citation statements)

References 36 publications

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“…Furthermore, tensor nuclear norm based regularizers that enforce channel-coupling are proposed in [42]. Material decomposition of multi-spectral CT images is considered in [36] and material decomposition of medical dual-energy CT images is considered in [18]. In [19], the authors propose a method to solve the nonlinear decomposition problem in spectral X-ray imaging.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi-channel Potts-based reconstruction for multi-spectral computed tomography

Kiefer¹,

Petra²,

Storath³

et al. 2021

Inverse Problems

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We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral x-ray computed tomography (CT). Our aim is to exploit the strong structural correlation that is known to exist between the channels of multi-spectral CT images. To that end, we adopt the multi-channel Potts prior to jointly reconstruct all channels. This nonconvex prior produces piecewise constant solutions with strongly correlated channels. In particular, edges are strictly enforced to have the same spatial position across channels which is a benefit over TV-based methods whose channel-couplings are typically less strict. We consider the Potts prior in two frameworks: (a) in the context of a variational Potts model, and (b) in a Potts-superiorization approach that perturbs the iterates of a basic iterative least squares solver. We identify an alternating direction method of multipliers approach as well as a Potts-superiorized conjugate gradient method as particularly suitable. In numerical experiments, we compare the Potts prior based approaches to existing TV-type approaches on realistically simulated multi-spectral CT data and obtain improved reconstruction for compound solid bodies.

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Section: Related Workmentioning

confidence: 99%

“…Data term. Concerning the data term D, it is common in CT to employ the penalized weighted least squares model (PWLS) [18,30,42]. The PWLS serves as a (computational more tractable) quadratic approximation of the log-likelihood function associated with the Poisson distribution in (10); see [41].…”

Section: Reconstructionmentioning

confidence: 99%

Multi-channel Potts-based reconstruction for multi-spectral computed tomography

Kiefer¹,

Petra²,

Storath³

et al. 2021

Inverse Problems

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“…Image-based decomposition can be combined with regularization to e.g. reduce noise [14,15,49,76]. A simulated example using monochromatic spectra is provided in Figure 3.…”

Section: Image-based Decompositionmentioning

confidence: 99%

Image Formation in Spectral Computed Tomography

Rit¹,

Mory²,

Noël³

2020

Spectral, Photon Counting Computed Tomography

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Spectral Computed Tomography (CT) can perform "color" x-ray detection; for example, photoncounting detectors can discriminate the energy of individual x-ray photons and divide them into several predefined energy bins, thereby providing a spectral analysis of the transmitted x-ray beam. By measuring the x-ray attenuation in two or more distinct energy bins, one can gain information about the elemental composition of an object, making it possible via material decomposition to distinguish between different materials, such as contrast agents and different types of tissues, in a single CT scan. This concept of spectral CT is based on the x-ray attenuation differences of various materials when simultaneously exposed by a spectrum of x-ray photons (which are emitted in a wide spectral range by a standard x-ray tube). Attenuation differences reflect the differences in material interactions with low-and high-energy photons, mainly Compton scatter and photoelectric effects in the diagnostic energy range. Interaction of x-rays with matter is described by the linear attenuation coefficient µ of an object, which depends on the three-dimensional (3D) position x in space and the one-dimensional (1D) energy of incident photons. The photon fluence Φ after the object of a monoenergetic pencil beam is described by the Beer-Lambert law

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“…The concept of dual-energy CT(DECT), 1 which fully utilizes the energy dependence feature of the x-ray attenuation, was proposed shortly after the invention of the first clinical CT. 2 Since then, many algorithms have been developed for dual-energy image reconstruction [3][4][5][6][7] and material decomposition. [8][9][10][11][12][13][14][15][16] Recent advances in commercial CT hardware, that is, fast kVpswitching, 17 dual-source dual-detector, 18 and dual-layer detector, 19 significantly promoted the clinical use of DECT, [20][21][22][23] which covers synthesis of monochromatic images, 4 virtual non-enhanced imaging, 24 automated bone removal, 25 kidney stone characterization, 26 and accurate reconstruction of electron density. 27 Flatpanel detector (FPD) based cone-beam CT (CBCT) has also grown rapidly, providing effective imaging tools for a wide range of clinical tasks such as imageguided radiotherapy 28 and interventions.…”

Section: Introductionmentioning

confidence: 99%

Fast and effective single‐scan dual‐energy cone‐beam CT reconstruction and decomposition denoising based on dual‐energy vectorization

Jiang

Fang

et al. 2021

Medical Physics

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Purpose Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.

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Image‐domain multimaterial decomposition for dual‐energy CT based on prior information of material images

Cited by 28 publications

References 36 publications

Multi-channel Potts-based reconstruction for multi-spectral computed tomography

Multi-channel Potts-based reconstruction for multi-spectral computed tomography

Image Formation in Spectral Computed Tomography

Fast and effective single‐scan dual‐energy cone‐beam CT reconstruction and decomposition denoising based on dual‐energy vectorization

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