Increasing the darkfield contrast-to-noise ratio using a deconvolution-based information retrieval algorithm in X-ray grating-based phase-contrast imaging

Weber, Thomas; Pelzer, Georg; Bayer, Florian; Horn, Florian; Rieger, Jens; Ritter, André; Zang, Andrea; Durst, Jürgen; Anton, G.; Michel, Thilo

doi:10.1364/oe.21.018011

Cited by 17 publications

(15 citation statements)

References 15 publications

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“…We used the standard deviation, std, within a 50 Â 50 pixel wide region of interest located in the background of the images to determine the performance of 2D FCA and 2D GI-USAXS. While we found negligible difference for the absorption ðstdðAÞ=stdðM 0 Þ ¼ 25 holds also true in the 2D case.…”

supporting

confidence: 61%

“…Thus, GI-USAXS can also be used as an alternative data analysis for obtaining the standard contrasts and it was shown that GI-USAXS provides a superior signal-to-noise ratio (SNR) for the dark-field contrast. 25 In this Letter, we combine the benefits of GI-USAXS with 2D-GI by utilizing two pairs of crossed line gratings ( (material: Si, pitch: g 1 ¼ 4.767 lm, and structure height: 32 lm) introduced a phase shift of p into the beam, which realized a checkerboard-type 2D phase grating and provided a mesh-like interference pattern at an inter-grating distance of z ¼ 174 mm. 26 At this position, the analyzer grating (material: Au, pitch: g 2 ¼ 2.4 lm, and structure height: 50 lm) was placed directly in front of the detector unit, which had a pixel size of 7.4 lm (pco.2000, PCO AG, Kelheim, Germany).…”

mentioning

confidence: 99%

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Two-dimensional ultra-small angle X-ray scattering with grating interferometry

Modregger

Rutishauser

Meiser

et al. 2014

Applied Physics Letters

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It was recently established that the pixel-wise ultra-small angle x-ray distribution can be retrieved with grating interferometry. However, in these one dimensional approaches the contrast was limited to the direction orthogonal to the structure of the line gratings. Here, we demonstrate that sensitivity in two contrast directions can be achieved by using two pairs of crossed line gratings and by adapting scan procedures and data analysis accordingly. We demonstrate the retrieval of two-dimensional scattering distributions with grating interferometry, thus overcoming the previously reported limit of seven obtainable, complementary contrasts. In addition, we give further evidence for the superiority of the signal-to-noise ratio for the dark-field contrast, if a deconvolution-based instead of the standard analysis is utilized.

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supporting

confidence: 61%

mentioning

confidence: 99%

Two-dimensional ultra-small angle X-ray scattering with grating interferometry

Modregger

Rutishauser

Meiser

et al. 2014

Applied Physics Letters

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“…Moreover, it is seen that the noise is still a little higher relative to the conventional iterative DB‐MMA in our preliminary experiment results. This is expected due to the smoothing effect by the Lucy–Richardson iterations in the deconvolution process in DB‐MMA . However, as shown in Table , the noise variance of the original D‐MMA can be eight times higher than that of the DB‐MMA in some extreme cases, while our optimal GD‐MMA reduces ∼40% noise magnification.…”

Section: Discussionmentioning

confidence: 75%

“…For the classical FCA method, Yashiro et al defined a figure of merit to assess the efficiency of capturing a DPC image with a large signal‐to‐noise (SNR) for a given incident photon number, while Chabior et al found that the statistical noise in DFC follows a Rician distribution . For DB‐MMA, Weber et al demonstrated that the resulted contrast‐to‐noise (CNR) can be higher than that of FCA, especially for DFC images . In this paper, we intend to find an optimal form for noise reduction among all the GD‐MMA forms proposed following the integral property of PSCs.…”

Section: Introductionmentioning

confidence: 99%

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The trigonometric orthogonality of phase‐stepping curves in grating‐based x‐ray phase‐contrast imaging: Integral property and its implications for noise optimization

Zhang

Chen

et al. 2019

Medical Physics

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Purpose Grating‐based x‐ray phase‐contrast imaging (GPCI) is a promising technique for clinical applications as it can provide two newly emerging imaging modalities (differential phase‐contrast and dark‐field contrast) in addition to the conventional absorption contrast. As far, phase‐stepping strategy is the most commonly used approach in GPCI to indirectly acquire differential phase‐contrast and dark‐field contrast. It is known that the obtained phase‐stepping curves (PSCs) have the cosine property and the convolution property, leading to two types of information retrieval approaches in literature: the Fourier component analysis and the multi‐order moment analysis. The purpose of this paper is to derive a new property of PSCs and apply the property to noise optimization for information retrieval. Methods Based on the cosine expression of the flat PSC without the sample and the well‐established convolution relationship between the flat PSC and the sample PSC, we reveal an important integral property of PSCs: the inner product of PSCs and an arbitrary function contains only zero‐order and first‐order components in the Fourier series. Furthermore, we apply the property to the direct multi‐order moment analysis and propose a set of generalized forms including an optimal one in the presence of noise. Results To validate the effectiveness of our analysis, we compared the simulated and real experiment results retrieved by the original direct multi‐order moment analysis with the ones retrieved by our proposed noise‐optimal form. A significant improvement of noise performance by our method is observed and the improvement ratio in differential phase‐contrast is consistent with our theoretical calculation (39.2%). Conclusions In this paper, we reveal a new integral property of the acquired PSCs with and without samples in GPCI, which can be applied to information retrieval approaches like the direct multi‐order moment analysis. Then we optimize these approaches to improve the noise performance, offering great potentials of dose reduction in practical applications.

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Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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Increasing the darkfield contrast-to-noise ratio using a deconvolution-based information retrieval algorithm in X-ray grating-based phase-contrast imaging

Cited by 17 publications

References 15 publications

Two-dimensional ultra-small angle X-ray scattering with grating interferometry

Two-dimensional ultra-small angle X-ray scattering with grating interferometry

The trigonometric orthogonality of phase‐stepping curves in grating‐based x‐ray phase‐contrast imaging: Integral property and its implications for noise optimization

Artificial intelligence in image reconstruction: The change is here

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