X-ray scattering tomography for biological applications

Cong, Wenxiang; Wang, G.

doi:10.3233/xst-2011-0288

Cited by 19 publications

(22 citation statements)

References 19 publications

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“…Poisson random variable with mean and variance 𝜆 𝜙 = 10 9 photons∕mm 2 ∕s for a total scan time of 1 s and 𝜆 𝜙 = 5 × 10 9 photons∕mm 2 ∕s for a scan time of 10 s in the phantom and medical image simulations, respectively. To capture stochastic noise during the simulation, pre-scatter attenuation, scattering, and postscatter attenuation were all treated as Poisson events, with only their expected values following the framework in Equations (1)(2)(3)(4)(5). While our reconstruction model approximates a global attenuation map, postscatter attenuation was treated as energy dependent during the simulation.…”

Section: Source Parametersmentioning

confidence: 99%

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Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

2021

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Creating a viable reconstruction method for Compton scatter tomography remains challenging. Accounting for scatter attenuation when the underlying attenuation map is not known is particularly challenging, and current mathematical approaches to this vary widely. This work explores a novel approach to joint scatter and attenuation image reconstruction, which leverages the underlying structural similarity between the two images and incorporates a deep learning model in an alternating iterative reconstruction scheme. Methods: A single-view computed tomography (CT) imaging procedure for recording Compton scatter is first described. A joint reconstruction model, which iterates between algebraically reconstructing scatter images and estimating the attenuation via deep learning, is then proposed. This model is tested on both a generated dataset of 2D phantom images designed to mimic human tissues as well as a realistically simulated dataset based on real CT images. Results: Testing results yield convergence of the model and decent reconstruction quality to distinguish crucial features such as tumors and lesions, demonstrating the potential principled utilities of this configuration and deep learning approach. The model achieved a structural similarity index measure of at least 0.82 for scatter and 0.88 for attenuation reconstructions with the realistically simulated dataset. Conclusion:The iterative, deep learning approach outlined in this work shows potential for future efficient medical imaging procedures, reconstructing images with limited scatter information.

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Section: Source Parametersmentioning

confidence: 99%

“…In 2001, Leclair and Johns showed the viability of X‐ray forward scatter for scatter image reconstruction using a plastic phantom 9 . In 2011, Cong and Wang proposed a compressed‐sensing‐based scatter imaging scheme 5 . Redler et al.…”

Section: Introductionmentioning

confidence: 99%

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

2021

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“…The noise is added as follows. First, we recall that the data function Φ is constructed as a linear superposition of functions φ jk , which, in turn, are related the physical measurements of intensity W jk by (5). We therefore add noise directly to the functions φ jk and then construct the data function of the star transform, Φ, according to (9).…”

Section: Simulationsmentioning

confidence: 99%

“…In the case of X-rays, account of scattering is simplified because, for the physical parameters encountered in typical applications, the single-scattering approximation can be used safely. All this has stimulated interest in using singlescattered photons for tomographic imaging [1][2][3][4][5][6]. We also note that, under suitable conditions, single-scattering regime can be applicable to optical imaging as well, e.g., in the mesoscopic scattering regime [7].…”

Section: Introductionmentioning

confidence: 99%

Inversion of the star transform

Zhao¹,

Schotland²,

Markel³

2014

Inverse Problems

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We define the star transform as a generalization of the broken ray transform introduced by us in previous work. The advantages of using the star transform include the possibility to reconstruct the absorption and the scattering coefficients of the medium separately and simultaneously (from the same data) and the possibility to utilize scattered radiation which, in the case of the conventional X-ray tomography, is discarded. In this paper, we derive the star transform from physical principles, discuss its mathematical properties and analyze numerical stability of inversion. In particular, it is shown that stable inversion of the star transform can be obtained only for configurations involving odd number of rays. Several computationally-efficient inversion algorithms are derived and tested numerically.

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“…The incident light is however heavily scattered inside a medium when the medium is optically thick; this usually happens in the case of human body. This problem is called scattering tomography and recently studied in optics [4,5], physics [6,7], computer vision [8,9], and even computer graphics [10,11].…”

Section: Introductionmentioning

confidence: 99%

Layered optical tomography of multiple scattering media with combined constraint optimization

Yuan

Tamaki

Kushida

et al. 2015

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)

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In this paper, we proposed an improved optical scattering tomography for optically dense media. We model a material by many layers with voxels, and light scattering by a distribution from a voxel in one layer to other voxels in the next layer. Then we write attenuation of light along a light path by an inner product of vectors, and formulate the scattering tomography as an inequality constraint optimization problem solved by an interior point method. To improve the accuracy, we solve simultaneously four configurations of a multiple-scattering tomography, however, this would increase the computational cost by a factor of four if we simply solved the problem four times. To reduce the computation cost, we introduce a quasi-Newton method to update the inverse of a Hessian matrix used in the iteration of the interior point method. We show experimental results with numerical simulation for evaluating the proposed method and comparisons with our previous work.

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X-ray scattering tomography for biological applications

Cited by 19 publications

References 19 publications

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

Feasibility analysis on simultaneous electron density and attenuation coefficient reconstruction

Inversion of the star transform

Layered optical tomography of multiple scattering media with combined constraint optimization

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