X-ray Dark-field Imaging Research Articles

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefactremoval. Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for trainingdata. We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging ofpatients. The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truthimages. Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinicalradiology.

A comparison of X-ray attenuation, differential phase, and dark-field contrast imaging for the detection of porosity in carbon fiber reinforced cyanate ester

In this work we explore the capabilities of Talbot-Lau grating interferometry (TLGI) radiography for the inspection of porosity in structural specimens of cyanate ester carbon fiber reinforced polymer. The influence of system resolution and varying specimen thicknesses on mean values and standard deviations (STDV) in all three image modalities acquired by TLGI are addressed. Results show that mean absorption contrast (AC) values are highly affected by specimen thickness and strong negative correlation (r ≤ −0.8) is found only after correction via preliminary thickness measurements. Although dark-field contrast (DFC) is affected by changes in specimen thickness as well, the signal can be corrected by normalization with the inherently available AC. Consequently, strong positive correlation with porosity was found both in high- and low-resolution imaging (r = 0.83 and 0.71 respectively). Without the need for high image resolution or thickness measurements, the normalized DFC is a promising option for large field of view inspections. Investigations of STDV revealed strong positive correlations between porosity and AC STDV as well as differential phase contrast (DPC) STDV (r = 0.95 and 0.92 respectively) but high image resolution is required. Furthermore, results suggest increased robustness against variations in specimen thickness of AC and DPC STDV analyses.

Nondestructive internal disorders detection of ‘Braeburn’ apple fruit by X-ray dark-field imaging and machine learning

'Braeburn' apples are susceptible to internal browning disorders when stored under controlled atmosphere (CA) conditions with unfavorable gas compositions. The progression of CA-related disorders in apple tissues is dynamic, noting a decrease in porosity during early storage due to cellular breakdown and pore flooding, and an increase in porosity in later stages due to structural collapse and cavity formation. Utilizing grating-based X-ray dark-field radiography, which leverages X-ray small-angle scattering to detect microstructural changes below the pixel scale, this study assesses the technique's efficacy in identifying internal disorders in 'Braeburn' apples at both early and later stages. A machine learning approach was applied to compare the diagnostic capabilities of dark-field imaging with those of X-ray absorption radiography at identical image resolutions. Results indicate that for early-stage disordered fruit detection, X-ray dark field radiography is 10 % more accurate than absorption radiography, regardless of the machine learning classifiers that were applied. In the later stage of browning, dark-field imaging performs similarly to absorption imaging. High-resolution micro-computed tomography scans suggested that the distinct detection performance of dark-field imaging may be attributed to the more pronounced microstructural differences between healthy and early-stage defective tissues than those between healthy and later-stage defective tissues. The insights from this work will guide the application of X-ray dark-field systems in fruit quality assurance, particularly in detecting internal disorders.

X-Ray Dark-Field Signal Reduction Due to Hardening of the Visibility Spectrum.

X-ray dark-field imaging enables a spatially-resolved visualization of ultra-small-angle X-ray scattering. Using phantom measurements, we demonstrate that a material's effective dark-field signal may be reduced by modification of the visibility spectrum by other dark-field-active objects in the beam. This is the dark-field equivalent of conventional beam-hardening, and is distinct from related, known effects, where the dark-field signal is modified by attenuation or phase shifts. We present a theoretical model for this group of effects and verify it by comparison to the measurements. These findings have significant implications for the interpretation of dark-field signal strength in polychromatic measurements.

X-ray dark-field computed tomography for monitoring of tissue freezing

Accurately monitoring the extent of freezing in biological tissue is an important requirement for cryoablation, a minimally invasive cancer treatment that induces cell death by freezing tissue with a cryoprobe. During the procedure, monitoring is required to avoid unnecessary harm to the surrounding healthy tissue and to ensure the tumor is properly encapsulated. One commonly used monitoring method is attenuation-based computed tomography (CT), which visualizes the ice ball by utilizing its hypoattenuating properties compared to unfrozen tissue. However, the contrast between frozen and unfrozen tissue remains low. In a proof-of-principle experiment, we show that the contrast between frozen and unfrozen parts of a porcine phantom mimicking breast tissue can be greatly enhanced by acquiring X-ray dark-field images that capture the increasing small-angle scattering caused by the ice crystals formed during the procedure. Our results show that, compared to X-ray attenuation, the frozen region is detected significantly better in dark-field radiographs and CT scans of the phantom. These findings demonstrate that X-ray dark-field imaging could be a potential candidate for improved monitoring of cryoablation procedures.

Synchrotron Radiation Refraction-Contrast Computed Tomography Based on X-ray Dark-Field Imaging Optics of Pulmonary Malignancy: Comparison with Pathologic Examination.

Refraction-contrast computed tomography based on X-ray dark-field imaging (XDFI) using synchrotron radiation (SR) has shown superior resolution compared to conventional absorption-based methods and is often comparable to pathologic examination under light microscopy. This study aimed to investigate the potential of the XDFI technique for clinical application in lung cancer diagnosis. Two types of lung specimens, primary and secondary malignancies, were investigated using an XDFI optic system at beamline BL14B of the High-Energy Accelerator Research Organization Photon Factory, Tsukuba, Japan. Three-dimensional reconstruction and segmentation were performed on each specimen. Refraction-contrast computed tomographic images were compared with those obtained from pathological examinations. Pulmonary microstructures including arterioles, venules, bronchioles, alveolar sacs, and interalveolar septa were identified in SR images. Malignant lesions could be distinguished from the borders of normal structures. The lepidic pattern was defined as the invasive component of the same primary lung adenocarcinoma. The SR images of secondary lung adenocarcinomas of colorectal origin were distinct from those of primary lung adenocarcinomas. Refraction-contrast images based on XDFI optics of lung tissues correlated well with those of pathological examinations under light microscopy. This imaging method may have the potential for use in lung cancer diagnosis without tissue damage. Considerable equipment modifications are crucial before implementing them from the lab to the hospital in the near future.

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BackgroundThe low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.MethodsEight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.ResultsThe nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).ConclusionsCompared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.Relevance statementDark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.Key points• Lung cancer progression was observed using x-ray dark-field CT.• Dark-field modality complements with attenuation modality in lung nodule detection.• Dark-field modality showed a detectability index higher than that attenuation in nodule detection.Graphical

Robust Sample Information Retrieval in Dark-Field Computed Tomography with a Vibrating Talbot-Lau Interferometer.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials' attenuation coefficients to generate contrast and provide 3D information. Grating-based dark-field-contrast X-ray imaging is an innovative technique that utilizes small-angle scattering to generate additional co-registered images with additional microstructural information. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of X-ray dark-field imaging and large field-of-view (FOV) tomography in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT prototype. We present the corrective measures that are applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying spatially and temporally variable vibrations in air reference scans. By translating the found correlations to the sample scan, we can identify and mitigate relevant fluctuation modes for scans with arbitrary sample sizes. This approach effectively eliminates the requirement for sample-free detector area, while still distinctly separating fluctuation and sample information. As a result, samples of arbitrary dimensions can be reconstructed without being affected by vibration artifacts. To demonstrate the viability of the technique for human-scale objects, we present reconstructions of an anthropomorphic thorax phantom.

Multi-resolution X-ray phase-contrast and dark-field tomography of human cerebellum with near-field speckles.

In this study, we use synchrotron-based multi-modal X-ray tomography to examine human cerebellar tissue in three dimensions at two levels of spatial resolution (2.3 µm and 11.9 µm). We show that speckle-based imaging (SBI) produces results that are comparable to propagation-based imaging (PBI), a well-established phase-sensitive imaging method. The different SBI signals provide complementary information, which improves tissue differentiation. In particular, the dark-field signal aids in distinguishing tissues with similar average electron density but different microstructural variations. The setup's high resolution and the imaging technique's excellent phase sensitivity enabled the identification of different cellular layers and additionally, different cell types within these layers. We also correlated this high-resolution phase-contrast information with measured dark-field signal levels. These findings demonstrate the viability of SBI and the potential benefit of the dark-field modality for virtual histology of brain tissue.

Direct x-ray scattering signal measurements in edge-illumination/beam-tracking imaging and their interplay with the variance of the refraction signals

X-ray dark-field or ultra-small angle scatter imaging has become increasingly important since the introduction of phase-based x-ray imaging and is having transformative impact in fields such as in vivo lung imaging and explosives detection. Here, we show that dark-field images acquired with the edge-illumination method (either in its traditional double mask or simplified single mask implementation) provide a direct measurement of the scattering function, which is unaffected by system-specific parameters such as the autocorrelation length. We show that this is a consequence both of the specific measurement setup and of the mathematical approach followed to retrieve the dark-field images. We show agreement with theoretical models for datasets acquired both with synchrotron and laboratory x-ray sources. We also introduce a new contrast mechanism, the variance of refraction, which is extracted from the same dataset and provides a direct link with the size of the scattering centers. We show that this can also be described by the same theoretical models. We study the behavior of both signals vs key parameters such as x-ray energy and scatterer radius. We find this allows quantitative and direct scattering measurements during imaging, with implications in all fields where dark-field imaging is used.

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.

Deep-learning-based denoising of X-ray differential phase and dark-field images

PurposeStatistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images. MethodsA novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities. ResultsExperimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively.Conclusions.The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.

Superimposed wavefront imaging of diffraction-enhanced x-rays: A method to achieve higher resolution in crystal analyzer-based x-ray phase-contrast imaging

In conventional phase-contrast imaging methods, an optimum distance that is essential for x-ray interference and wavefront separation must be maintained between the object and the x-ray detector. This distance determines the apparent focal size of the x-ray source and affects spatial resolution of the system. To improve the spatial resolution, we proposed a phase-contrast imaging method that eliminates the distance required for x-ray interference and wavefront separation by placing a scintillator in close contact with a Laue angle analyzer (LAA). We mathematically described the image formed by superposition of diffracted beams in two directions of LAA and introduced a method to reconstruct tomographic sections from multiple projections captured by this setup. We experimentally demonstrated the validity and the usefulness of the proposed method. Using a physical phantom, we showed that the proposed method provides higher spatial resolution than conventional x-ray dark-field imaging. The proposed method may be useful for studying and elucidating the fine 3D microanatomy and histopathology of various biological specimens.

Spectral X-ray dark-field signal characterization from dual-energy projection phase-stepping data with a Talbot-Lau interferometer

Material-selective analysis of spectral X-ray imaging data requires prior knowledge of the energy dependence of the observed signal. Contrary to conventional X-ray imaging, where the material-specific attenuation coefficient is usually precisely known, the linear diffusion coefficient of the X-ray dark-field contrast does not only depend on the material and its microstructure, but also on the setup geometry and is difficult to access. Here, we present an optimization approach to retrieve the energy dependence of the X-ray dark-field signal quantitatively on the example of closed-cell foams from projection data without the need for additional hardware to a standard grating-based X-ray dark-field imaging setup. A model for the visibility is used to determine the linear diffusion coefficient with a least-squares optimization. The comparison of the results to spectrometer measurements of the linear diffusion coefficient suggests the proposed method to provide a good estimate for the energydependent dark-field signal.

Exploration of the X-ray Dark-Field Signal in Mineral Building Materials.

Mineral building materials suffer from weathering processes such as salt efflorescence, freeze–thaw cycling, and microbial colonization. All of these processes are linked to water (liquid and vapor) in the pore space. The degree of damage following these processes is heavily influenced by pore space properties such as porosity, pore size distribution, and pore connectivity. X-ray computed micro-tomography (µCT) has proven to be a valuable tool to non-destructively investigate the pore space of stone samples in 3D. However, a trade-off between the resolution and field-of-view often impedes reliable conclusions on the material’s properties. X-ray dark-field imaging (DFI) is based on the scattering of X-rays by sub-voxel-sized features, and as such, provides information on the sample complementary to that obtained using conventional µCT. In this manuscript, we apply X-ray dark-field tomography for the first time on four mineral building materials (quartzite, fired clay brick, fired clay roof tile, and carbonated mineral building material), and investigate which information the dark-field signal entails on the sub-resolution space of the sample. Dark-field tomography at multiple length scale sensitivities was performed at the TOMCAT beamline of the Swiss Light Source (Villigen, Switzerland) using a Talbot grating interferometer. The complementary information of the dark-field modality is most clear in the fired clay brick and roof tile; quartz grains that are almost indistinguishable in the conventional µCT scan are clearly visible in the dark-field owing to their low dark-field signal (homogenous sub-voxel structure), whereas the microporous bulk mass has a high dark-field signal. Large (resolved) pores on the other hand, which are clearly visible in the absorption dataset, are almost invisible in the dark-field modality because they are overprinted with dark-field signal originating from the bulk mass. The experiments also showed how the dark-field signal from a feature depends on the length scale sensitivity, which is set by moving the sample with respect to the grating interferometer.

Investigations of moiré artifacts induced by flux fluctuations in x-ray dark-field imaging

X-ray dark-field imaging using a grating interferometer has shown potential benefits for a variety of applications in recent years. X-ray dark-field image is commonly retrieved by using discrete Fourier transform from the acquired phase-stepping data. The retrieval process assumes a constant phase step size and a constant flux for each stepped grating position. However, stepping errors and flux fluctuations inevitably occur due to external vibrations and/or thermal drift during data acquisition. Previous studies have shown that those influences introduce errors in the acquired phase-stepping data, which cause obvious moiré artifacts in the retrieved refraction image. This work investigates moiré artifacts in x-ray dark-field imaging as a result of flux fluctuations. For the retrieved mean intensity, amplitude, visibility and dark-field images, the dependence of moiré artifacts on flux fluctuation factors is theoretically derived respectively by using a first-order Taylor series expansion. Results of synchrotron radiation experiments verify the validity of the derived analytical formulas. The spatial frequency characteristics of moiré artifacts are analyzed and compared to those induced by phase-stepping errors. It illustrates that moiré artifacts can be estimated by a weighted mean of flux fluctuation factors, with the weighting factors dependent on the moiré phase and different greatly for each retrieved image. Furthermore, moiré artifacts can even be affected by object’s features not displayed in the particular contrast. These results can be used to interpret images correctly, identify sources of moiré artifacts, and develop dedicated algorithms to remove moiré artifacts in the retrieved multi-contrast images.

X-ray Dark-Field Imaging for Improved Contrast in Historical Handwritten Literature.

If ancient documents are too fragile to be opened, X-ray imaging can be used to recover the content non-destructively. As an extension to conventional attenuation imaging, dark-field imaging provides access to microscopic structural object information, which can be especially advantageous for materials with weak attenuation contrast, such as certain metal-free inks in paper. With cotton paper and different self-made inks based on authentic recipes, we produced test samples for attenuation and dark-field imaging at a metal-jet X-ray source. The resulting images show letters written in metal-free ink that were recovered via grating-based dark-field imaging. Without the need for synchrotron-like beam quality, these results set the ground for a mobile dark-field imaging setup that could be brought to a library for document scanning, avoiding long transport routes for valuable historic documents.

Envelope modulated x-ray grating interferometry

X-ray dark-field and phase contrast imaging using grating interferometry (GI) have demonstrated great potential for medical and industrial applications. GI relies on the fabrication of high-quality absorption gratings, which has revealed to be quite challenging. This paper proposes an interferometer for dark-field and differential phase contrast imaging using a single phase-shifting element. This proposal simplifies the setup without penalizing the image quality. The proposed interferometer consists of a single dual-period phase grating having two distinct periods, which generates intensity modulation with a period large enough to be resolved directly by the detector. This makes the proposed GI system easily adaptable as there are no stringent alignment requirements. We show the utility of this method for a possible diagnostic task by imaging smoked murine lung samples. The simplicity of the proposed setup compared to a conventional Talbot(-Lau) interferometer suggests that it could be easily and widely used for a variety of applications.

Non-destructive characterisation of out-of-plane fibre waviness in carbon fibre reinforced polymers by X-ray dark-field radiography

ABSTRACT Fibre waviness is a frequently encountered problem in composite design and manufacturing as it can severely influence mechanical properties of components. In this work, we propose a new method for the detection and quantification of out-of-plane fibre waviness in carbon fibre composites using Talbot-Lau grating interferometry. The sensitivity of X-ray dark-field imaging to the orientation of carbon fibres is exploited to visualise fibre waviness by radiographic imaging and reduce measurement times in comparison to full computed tomography scans. We show that fibre waviness can be qualitatively detected by single radiographic images providing a valid option, for example, for in-line monitoring of similar specimens. Furthermore, quantitative evaluation of fibre waviness angles can be performed by repeated radiographic imaging over an angular range of roughly 10°. Therefore, the number of required projection images can be reduced significantly. With this method, we evaluated wave angles at an error of less than ±1.5° compared to results achieved by full computed tomography scans.

Quantifying the x-ray dark-field signal in single-grid imaging.

X-ray dark-field imaging reveals the sample microstructure that is unresolved when using conventional methods of x-ray imaging. In this paper, we derive a new method to extract and quantify the x-ray dark-field signal collected using a single-grid imaging set-up, and relate the signal strength to the number of sample microstructures, N. This was achieved by modelling sample-induced changes to the shadow of the upstream grid, and fitting experimental data to this model. Our results suggested that the dark-field scattering angle from our spherical microstructures deviates slightly from the theoretical model of N, which was consistent with results from other experimental methods. We believe the approach outlined here can equip quantitative dark-field imaging of small samples, particularly in cases where only one sample exposure is possible, either due to sample movement or radiation dose limitations. Future directions include an extension into directional dark-field imaging.