Snapshot 2D tomography via coded aperture x-ray scatter imaging

MacCabe, Kenneth P.; Holmgren, Andrew; Tornai, Martin P.; Brady, David J.

doi:10.1364/ao.52.004582

Cited by 47 publications

(22 citation statements)

References 27 publications

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“…Transmittance is the quantity of energy that passes through an object, for this case, it is the magnitude that expresses the quantity of X-ray light that is definitely projected over an object [20]. The coded apertures have been developed and tested on X-ray systems based on fan beam geometry [21]. The coded pattern is modeled in Matlab, a mold of the aperture is printed and is filled with Tungsten powder and it is sealed with epoxy resin.…”

Section: Coded Aperture Designmentioning

confidence: 99%

Sources and Coded Aperture Transmittance Analysis in Compressive Computed Tomography

Mendoza

Melgarejo

Fuentes

2016

I&U

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Section: Coded Aperture Designmentioning

confidence: 99%

Sources and Coded Aperture Transmittance Analysis in Compressive Computed Tomography

Mendoza

Melgarejo

Fuentes

2016

I&U

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“…A Gaussian measurement model is not appropriate for many important applications, including X-ray [9,20] and chemical imaging [29,30,31]. The observed data in these applications are characterized by counts, typically under Poisson statistics.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…The coded aperture modulates the scattered photons in a manner that depends on the position of emission and on the angle of scatter, helping to disambiguate the source of observed photons when seeking to recover f k from y k . Further details on the X-ray system may be found in [9,20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Wang

Huang

Yuan

et al. 2015

SIAM J. Imaging Sci.

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Abstract. The measurement matrix employed in compressive sensing typically cannot be known precisely a priori, and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be estimated jointly. This is of interest as well when the measurement matrix may change as a function of the details of what is measured. This problem has been considered recently for Gaussian measurement noise, and here we develop this idea with application to Poisson systems. A collaborative maximum likelihood algorithm and alternating proximal gradient algorithm are proposed, and associated theoretical performance guarantees are established based on newly derived concentration-of-measure results. A Bayesian model is then introduced, to improve flexibility and generality. Connections between the maximum likelihood methods and the Bayesian model are developed, and example results are presented for a real compressive X-ray imaging system.

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“…For example, we have shown that 3D hyperspectral [18], diffraction [19], and x-ray scatter [20,21] images may be reconstructed from 2D data. We have also analyzed compressive sampling for reconstruction of 3D objects with conventional optics [22].…”

Section: Introductionmentioning

confidence: 99%

Compressed sampling strategies for tomography

Kaganovsky

Holmgren

et al. 2014

J. Opt. Soc. Am. A

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We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels. We propose a new approach based on quasi-random detector subsampling, whereas previous approaches only addressed subsampling with respect to source location (view angle). The performance of different sampling strategies is considered using object-independent figures of merit, and also based on reconstructions for specific objects, with synthetic and real data. The proposed approach can be implemented using a structured illumination of the interrogated object or the detector array by placing a coded aperture/mask at the source or detector side, respectively. Advantages of the proposed approach include (i) for structured illumination of the detector array, it leads to fewer detector pixels and allows one to integrate detectors for scattered radiation in the unused space; (ii) for structured illumination of the object, it leads to a reduced radiation dose for patients in medical scans; (iii) in the latter case, the blocking of rays reduces scattered radiation while keeping the same energy in the transmitted rays, resulting in a higher signal-to-noise ratio than that achieved by lowering exposure times or the energy of the source; (iv) compared to view-angle subsampling, it allows one to use fewer measurements for the same image quality, or leads to better image quality for the same number of measurements. The proposed approach can also be combined with view-angle subsampling.

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Snapshot 2D tomography via coded aperture x-ray scatter imaging

Cited by 47 publications

References 27 publications

Sources and Coded Aperture Transmittance Analysis in Compressive Computed Tomography

Sources and Coded Aperture Transmittance Analysis in Compressive Computed Tomography

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Compressed sampling strategies for tomography

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