Evidence map›Paper›PMID 28894764›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2017

Improved reconstruction of phase-stepping data for Talbot-Lau x-ray imaging.

Sebastian Kaeppler, Jens Rieger, Georg Pelzer, Florian Horn, Thilo Michel, Andreas Maier, Gisela Anton, Christian Riess

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Sebastian KaepplerFriedrich-Alexander-University Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Erlangen, Germany.
Jens RiegerFriedrich-Alexander-University Erlangen-Nuremberg, Erlangen Centre for Astroparticle Physics, Department of Physics, Erlangen, Germany.
Georg PelzerFriedrich-Alexander-University Erlangen-Nuremberg, Erlangen Centre for Astroparticle Physics, Department of Physics, Erlangen, Germany.
Florian HornFriedrich-Alexander-University Erlangen-Nuremberg, Erlangen Centre for Astroparticle Physics, Department of Physics, Erlangen, Germany.
Thilo MichelFriedrich-Alexander-University Erlangen-Nuremberg, Erlangen Centre for Astroparticle Physics, Department of Physics, Erlangen, Germany.
Andreas MaierFriedrich-Alexander-University Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Erlangen, Germany.
Gisela AntonFriedrich-Alexander-University Erlangen-Nuremberg, Erlangen Centre for Astroparticle Physics, Department of Physics, Erlangen, Germany.
Christian RiessFriedrich-Alexander-University Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Erlangen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grating-based Talbot-Lau x-ray interferometry is a popular method for measuring absorption, phase shift, and small-angle scattering. The standard acquisition method for this modality is phase stepping, where the Talbot pattern is reconstructed from multiple images acquired at different grating positions. We review the implicit assumptions in phase-stepping reconstruction, and find that the assumptions of perfectly known grating positions and homoscedastic noise variance are violated in some scenarios. Additionally, we investigate a recently reported estimation bias in the visibility and dark-field signal. To adapt the phase-stepping reconstruction to these findings, we propose three improvements to the reconstruction. These improvements are (a) to use prior knowledge to compute more accurate grating positions to reduce moiré artifacts, (b) to utilize noise variance information to reduce dark-field and phase noise in high-visibility acquisitions, and (c) to perform correction of an estimation bias in the interferometer visibility, leading to more quantitative dark-field imaging in acquisitions with a low signal-to-noise ratio. We demonstrate the benefit of our methods on simulated data, as well as on images acquired with a Talbot-Lau interferometer.

Indexed as

moiréphase contrastphase steppingreconstructionTalbot–Lau

Identifiers

PMID28894764
PMCPMC5584710

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.