Signal and noise analysis of flat-panel sandwich detectors for single-shot dual-energy x-ray imaging

A: A novel sandwich-style single-shot detector has been built by stacking two indirectconversion flat-panel detectors for preclinical dual-energy mouse imaging. Although this single-shot method is more immune to motion artifacts compared with the conventional dual-shot method (i.e., fast kVp switching), it may suffer from reduced image quality because of poor spectral separation between the two detectors. Spectral separation can be improved by using an intermediate filter between the two detector layers. Adversely, the filter reduces the number of x-ray photons reaching the rear detector, hence probably increasing image noise. For a better design and practical use of the sandwich detector for single-shot dual-energy imaging, imaging performances of each detector layer in the sandwich detector are investigated for various spectral-separation extents and applied tube voltages. The imaging performances include the modulation-transfer function, the Wiener noise-power spectrum, and the detective quantum efficiency. According to the experimental results, impacts of the intermediate filter on the imaging performances of each detector layer are marginal. The detailed experimental results are shown in this study.

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“…The DQE of the jth detector layer ( j designates F or R for the front or rear detector, respectively) is given by [14]…”

Section: Detective Quantum Efficiencymentioning

confidence: 99%

Effects of the energy-separation filter on the performance of each detector layer in the sandwich detector for single-shot dual-energy imaging

Kim

Kam

et al. 2016

J. Inst.

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“…This imaging mode discriminates photon energies in the data acquisition process, and provides multiple projection datasets of an object in different energy bins, which can be achieved using different techniques such as a photon counting detector (PCD) [1], a sandwich (multi-layer) detector [2], a kV switching [3], [4], a multiple x-ray source [5], etc . The multichannel datasets can be utilized to generate material basis images for material decomposition [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Dictionary Learning for Spectral CT Reconstruction

Zhang

Mou

Wang

et al. 2017

IEEE Trans. Med. Imaging

174

136

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Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods.

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“…Spectral CT, or multi-energy CT, has attracted increasing attention in recent years because of its ability in discriminating different materials. Dual energy CT is a simple realization of spectral CT that differentiates material by using two distinct beam energies, including a dual x-ray source (Graser et al , 2009), a fast kVp switching (Zou and Silver, 2008), a dual layer detector (Kim et al , 2015a), etc . Another approach to realize spectral CT is to use a photon counting detector (PCD) with energy discrimination capability (Taguchi and Iwanczyk, 2013).…”

Section: Introductionmentioning

confidence: 99%

Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

Niu

et al. 2018

Inverse Problems

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Spectral computed tomography (CT) has been a promising technique in research and clinic because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifacts suppression and resolution preservation.

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Signal and noise analysis of flat-panel sandwich detectors for single-shot dual-energy x-ray imaging

Cited by 6 publications

References 19 publications

Effects of the energy-separation filter on the performance of each detector layer in the sandwich detector for single-shot dual-energy imaging

Effects of the energy-separation filter on the performance of each detector layer in the sandwich detector for single-shot dual-energy imaging

Tensor-Based Dictionary Learning for Spectral CT Reconstruction

Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

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