Energy-resolved CT imaging with a photon-counting silicon-strip detector

Persson, Mats; Huber, Ben; Karlsson, Stig; Liu, Xuejin; Chen, Han; Cheng, Xu; Yveborg, Moa; Bornefalk, Hans; Danielsson, Mats

doi:10.1117/12.2043519

Cited by 24 publications

(38 citation statements)

References 6 publications

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“…(1). For more energy bins (as can be obtained using, e.g., the PCD-CT scanner described by Persson et al 39 ), it can be assumed that the fit will be more robust if energy bins are merged, for example, by averaging the CT numbers for two or more energy bins. It has been shown that the linear behavior of the stoichiometric method makes it very robust to noise.…”

Section: Discussionmentioning

confidence: 99%

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

Taasti

Hansen

Michalak³

et al. 2018

Medical Physics

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Section: Discussionmentioning

confidence: 99%

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

Taasti

Hansen

Michalak³

et al. 2018

Medical Physics

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“…The transfer function is then Hðu; E; eÞ ¼ ð1 À p F ðEÞÞdðE À eÞ þ p F ðEÞdðE À E F À eÞ and an incident spectrum qðEÞ results in a measured spectrum dðeÞ ¼ ð1 À pðeÞÞ qðeÞ þ pðe þ E F Þ qðe þ E F Þ. Since each photon is only registered once, the measurements at different energies are independent random variables, and the cross-spectral density is given by Wðu; e; e 0 Þ ¼ dðeÞdðe À e 0 Þ. Inserting these results into (8) gives, after some algebra,…”

Section: A Fluorescence Modelmentioning

confidence: 99%

“…The NEQ and DQE matrices were calculated in the basis of monoenergies using Eqs. (8) and (12) and in the basis B ¼ fWater; Boneg using Eqs. (19) and (20).…”

Section: Cdte Simulationmentioning

confidence: 99%

“…[1][2][3][4][5] Systems with such detectors are currently commercially available for mammography, 6 and prototypes have been demonstrated for computed tomography (CT). [7][8][9][10] An energy-resolving photon-counting detector uses several electronic threshold levels to separate the registered counts into energy bins depending on the deposited energy from each event. This enables spectroscopic imaging, that is, using the information contained in the energy distribution of the incoming spectrum to obtain more information about the imaged object.…”

Section: Introductionmentioning

confidence: 99%

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A framework for performance characterization of energy‐resolving photon‐counting detectors

2018

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We have developed a framework for assessing the performance of photon-counting energy-resolving detectors and shown that the matrix-valued NEQ and DQE metrics contain sufficient information for calculating the dose efficiency for both detection and quantification tasks, the task having any spatial and energy dependence. This framework will be beneficial for the development and optimization of photon-counting x-ray detectors.

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“…Development of sophisticated correction schemes to alleviate this problem is a topic of ongoing research. [20][21][22] Direct inversion and projection-based approaches both rely on a mathematical description of the physics of image formation. Direct inversion methods recover the material density directly from the measured energy-resolved sinograms while the projection-based methods first decompose the energy-resolved sinograms into material-specific sinograms and then perform a tomographic reconstruction of each of the decomposed sinograms.…”

Section: Introductionmentioning

confidence: 99%

Regularization of nonlinear decomposition of spectral x‐ray projection images

et al. 2017

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Purpose: Exploiting the x-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3-D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a nonlinear ill-posed problem. Methods: Our main contribution is to introduce a material-dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss-Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss-Newton algorithm (RWLS-GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4-bin photon counting detector. To evaluate the method performance of our algorithm, different scenarios are created by varying the number of incident photons, the concentration of the marker and the configuration of the phantom. The RWLS-GN method is compared to the reference maximum likelihood Nelder-Mead algorithm (ML-NM). The convergence of the proposed method and its dependence on the regularization parameter are also studied. Results: We show that material decomposition is feasible with the proposed method and that it converges in few iterations. Material decomposition with ML-NM was very sensitive to noise, leading to decomposed images highly affected by noise, and artifacts even for the best case scenario. The proposed method was less sensitive to noise and improved contrast-to-noise ratio of the gadolinium image. Results were superior to those provided by ML-NM in terms of image quality and decomposition was 70 times faster. For the assessed experiments, material decomposition was possible with the proposed method when the number of incident photons was equal or larger than 10 5 and when the marker concentration was equal or larger than 0.03 gÁcm À3 . Conclusions: The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material-specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel.

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Energy-resolved CT imaging with a photon-counting silicon-strip detector

Cited by 24 publications

References 6 publications

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

A framework for performance characterization of energy‐resolving photon‐counting detectors

Regularization of nonlinear decomposition of spectral x‐ray projection images

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