Snapshot fan beam coded aperture coherent scatter tomography

Hassan, Mehadi; Greenberg, Joel A.; Odinaka, Ikenna; Brady, David J.

doi:10.1364/oe.24.018277

Cited by 26 publications

(26 citation statements)

References 21 publications

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“…Recent advances in coded aperture coherent scatter tomography [9] have demonstrated the use of a fan beam of interrogating X-rays to ultimately, exploit object motion to produce a threedimensional materials discriminating imaging system. Although, significant challenges in terms of spatially complex objects and cluttered scenes, which are prevalent in real-world applications, require further investigation and development.…”

Section: Introductionmentioning

confidence: 99%

X-ray absorption tomography employing a conical shell beam

Evans¹,

Godber²,

Elarnaut³

et al. 2016

Opt. Express

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Abstract:We demonstrate depth-resolved absorption imaging by scanning an object through a conical shell of X-rays. We measure ring shaped projections and apply tomosynthesis to extract optical sections at different axial focal plane positions. Three-dimensional objects have been imaged to validate our theoretical treatment. The novel principle of our method is scalable with respect to both scan size and X-ray energy. A driver for this work is to complement previously reported methods concerning the measurement of diffracted X-rays for structural analysis. The prospect of employing conical shell beams to combine both absorption and diffraction modalities would provide enhanced analytical utility and has many potential applications in security screening, process control and diagnostic imaging.

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Section: Introductionmentioning

confidence: 99%

X-ray absorption tomography employing a conical shell beam

Evans¹,

Godber²,

Elarnaut³

et al. 2016

Opt. Express

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“…Another interesting instance is ghost imaging, whose ML implementation we discuss in Section 4.C. Similarly, coded aperture approaches are effective for designing the pupil function in H c to improve performance [6,[55][56][57][58].…”

Section: A General Formulationmentioning

confidence: 99%

On the use of deep learning for computational imaging

Barbastathis

Ozcan

Situ

2019

Optica

555

225

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Since their inception in the 1930-1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or uncertain. Examples reviewed here are: super-resolution; lensless retrieval of phase and complex amplitude from intensity; photon-limited scenes, including ghost imaging; and imaging through scatter. In this paper, we cast these works in a common framework. We relate the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability. We also explore how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe. It is hoped that the present unifying exposition will stimulate further progress in this promising field of research.

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“…A practical scanner requires exposure times of the order of seconds or less per measurement. There has been a considerable effort in developing high-energy methods utilizing novel X-ray beam topologies [2,3,[7][8][9][10][11][12] and/or post-sample encoders [4,5,[12][13][14][15]. Focal construct geometry (FCG) is an example of the former, which exploits the 'lensing' of diffracted flux by extended gauge volumes produced by a conical shell beam of radiation incident on a sample.…”

Section: Introductionmentioning

confidence: 99%

Confocal energy-dispersive X-ray diffraction tomography employing a conical shell beam

Dicken¹,

Evans²,

Rogers³

et al. 2019

Opt. Express

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We introduce a new high-energy X-ray diffraction tomography technique for volumetric materials characterization. In this method, a conical shell beam is raster scanned through the samples. A central aperture optically couples the diffracted flux from the samples onto a pixelated energy-resolving detector. Snapshot measurements taken during the scan enable the construction of depth-resolved dark-field section images. The calculation of dspacing values enables the mapping of material phase in a volumetric image. We demonstrate our technique using five ~15 mm thick, axially separated samples placed within a polymer tray of the type used routinely in airport security stations. Our method has broad analytical utility due to scalability in both scan size and X-ray energy. Additional application areas include medical diagnostics, materials science, and process control.

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Snapshot fan beam coded aperture coherent scatter tomography

Cited by 26 publications

References 21 publications

X-ray absorption tomography employing a conical shell beam

X-ray absorption tomography employing a conical shell beam

On the use of deep learning for computational imaging

Confocal energy-dispersive X-ray diffraction tomography employing a conical shell beam

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