General reconstruction formulas for analyzer-based computed tomography

Nesterets, Yakov; Gureyev, T. E.; Wilkins, S. W.

doi:10.1063/1.2423325

Cited by 5 publications

(4 citation statements)

References 10 publications

(6 reference statements)

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“…The phase-imaging method employed here is equivalent to x-ray grating interferometry or analyzer-based imaging as the measured signal is the differential phase signal ∇φ [6,8,28]. In these methods, a specific filter is often used in the inverse Radon transform to account for the specificity of the signal and perform a one-dimensional integration in the Fourier space [29,30]. Here, as the XSVT technique provides the two transverse differential phase maps, in contrast to the previous techniques, the phase images can be recovered by 2D integration by matrix inversion via, for instance, the Cholesky decomposition.…”

Section: Xsvt-based Tomography With An Interlaced Schemementioning

confidence: 99%

X-ray Multimodal Tomography Using Speckle-Vector Tracking

Berujon

Ziegler

2016

Phys. Rev. Applied

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We demonstrate computerized tomography (CT) reconstructions from absorption, phase and darkfield signals obtained from scans acquired when the x-ray probe light is modulated with speckle. Two different interlaced schemes are proposed to reduce the number of sample exposures. First, the already demonstrated x-ray speckle-vector tracking (XSVT) concept for projection imaging allows the three signal CT reconstructions from multiple images per projection. Second, a modified XSVT approach is shown to provide absorption and phase reconstructions, this time from a single image per angular projection. Reconstructions from data obtained at a synchrotron facility emphasize the potential of the approaches for the imaging of complex samples.

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Section: Xsvt-based Tomography With An Interlaced Schemementioning

confidence: 99%

X-ray Multimodal Tomography Using Speckle-Vector Tracking

Berujon

Ziegler

2016

Phys. Rev. Applied

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“…However, in the research of phase contrast imaging, the FBP method has been commonly exploited in CT reconstruction [15-17]. Applying the FBP method requires a large amount of projection data, which can prolong scanning time and cumulate with a high dose of radiation potentially damaging the biological specimens.…”

Section: Introductionmentioning

confidence: 99%

“…Phase contrast X-ray imaging [ 9 - 13 ] enables the observation of light samples, such as biological soft tissue, without a contrast agent, because the phase shift cross sections of light elements are much larger than their absorption cross sections [ 14 ]. However, in the research of phase contrast imaging, the FBP method has been commonly exploited in CT reconstruction [ 15 - 17 ]. Applying the FBP method requires a large amount of projection data, which can prolong scanning time and cumulate with a high dose of radiation potentially damaging the biological specimens.…”

Section: Introductionmentioning

confidence: 99%

A compressed sensing-based iterative algorithm for CT reconstruction and its possible application to phase contrast imaging

Luo

2011

BioMedical Engineering OnLine

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BackgroundComputed Tomography (CT) is a technology that obtains the tomogram of the observed objects. In real-world applications, especially the biomedical applications, lower radiation dose have been constantly pursued. To shorten scanning time and reduce radiation dose, one can decrease X-ray exposure time at each projection view or decrease the number of projections. Until quite recently, the traditional filtered back projection (FBP) method has been commonly exploited in CT image reconstruction. Applying the FBP method requires using a large amount of projection data. Especially when the exposure speed is limited by the mechanical characteristic of the imaging facilities, using FBP method may prolong scanning time and cumulate with a high dose of radiation consequently damaging the biological specimens.MethodsIn this paper, we present a compressed sensing-based (CS-based) iterative algorithm for CT reconstruction. The algorithm minimizes the l1-norm of the sparse image as the constraint factor for the iteration procedure. With this method, we can reconstruct images from substantially reduced projection data and reduce the impact of artifacts introduced into the CT reconstructed image by insufficient projection information.ResultsTo validate and evaluate the performance of this CS-base iterative algorithm, we carried out quantitative evaluation studies in imaging of both software Shepp-Logan phantom and real polystyrene sample. The former is completely absorption based and the later is imaged in phase contrast. The results show that the CS-based iterative algorithm can yield images with quality comparable to that obtained with existing FBP and traditional algebraic reconstruction technique (ART) algorithms.DiscussionCompared with the common reconstruction from 180 projection images, this algorithm completes CT reconstruction from only 60 projection images, cuts the scan time, and maintains the acceptable quality of the reconstructed images.

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“…The second step has so far been solved by using a filtered back-projection (FBP) algorithm with an imaginary Hilbert filter (Pfeiffer et al 2007b). Meanwhile, this class of algorithm has also been extended to fan beam and cone beam scanning geometries by several groups (Faris and Byer 1988, Nesterets et al 2006, Pfeiffer et al 2007b, 2008b, Jerjen et al 2010.…”

Section: Introductionmentioning

confidence: 99%

A reconstruction method for equidistant fan beam differential phase contrast computed tomography

Wang

et al. 2011

Phys. Med. Biol.

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We report a reconstruction method, called a back-projection filtered (BPF) algorithm, for fan beam differential phase contrast computed tomography (DPC-CT) with equidistant geometrical configuration. This work comprises a numerical study of the algorithm and its experimental verification with a three-grating interferometer and an x-ray tube source. The numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated datasets. It could be of interest in future medical phase contrast imaging applications.

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General reconstruction formulas for analyzer-based computed tomography

Cited by 5 publications

References 10 publications

X-ray Multimodal Tomography Using Speckle-Vector Tracking

X-ray Multimodal Tomography Using Speckle-Vector Tracking

A compressed sensing-based iterative algorithm for CT reconstruction and its possible application to phase contrast imaging

A reconstruction method for equidistant fan beam differential phase contrast computed tomography

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