2017
DOI: 10.1103/physreva.95.063822
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Near-field speckle-scanning-based x-ray tomography

Abstract: We previously demonstrated near-field speckle scanning based x-ray imaging to be an easy-toimplement phase sensing method capable of providing both high sensitivity and high resolution. Yet, this performance combination could only be achieved at the cost of a significant number of sample exposures and of extensive data acquisition time, thus tempering its implementation for tomography applications. Here, we show ways of drastically lowering the number of exposures for the speckle scanning method to become attr… Show more

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Cited by 22 publications
(30 citation statements)
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“…Moreover, still several dozens of diffuser positions are needed for a successful reconstruction. Recently, a stepping scheme has been proposed that uses the concept of 2D XSS, but with a sparse sampling for the sample scan [156]. The acquisition of the reference patterns without the specimen in the beam is performed with a 2D raster scan of the diffuser as for classic 2D XSS, while sample images are taken only at every n-th point of the diffuser scanning grid of the reference scan.…”
Section: D Scanning With Sparse Samplingmentioning
confidence: 99%
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“…Moreover, still several dozens of diffuser positions are needed for a successful reconstruction. Recently, a stepping scheme has been proposed that uses the concept of 2D XSS, but with a sparse sampling for the sample scan [156]. The acquisition of the reference patterns without the specimen in the beam is performed with a 2D raster scan of the diffuser as for classic 2D XSS, while sample images are taken only at every n-th point of the diffuser scanning grid of the reference scan.…”
Section: D Scanning With Sparse Samplingmentioning
confidence: 99%
“…This significantly reduces the scan time and dose to the sample. However, a four-fold reduction of the sensitivity has been reported for the 5 × 5-step sparse scanning scheme compared to a conventional 25 × 25-step 2D XSS scan [156].…”
Section: D Scanning With Sparse Samplingmentioning
confidence: 99%
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