Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography.

As iterative reconstruction in Computed Tomography (CT) is an ill-posed problem, additional prior information has to be used to get a physically meaningful result (close to ground truth if available). However, the amount of influence of the regularisation prior is crucial to the outcome of the reconstruction. Therefore, we propose a scheme for tuning the strength of the prior via a certain image metric. In this work, the parameter is tuned for minimal histogram entropy in selected regions of the reconstruction as histogram entropy is a very basic approach to characterise the information content of data. We performed a sweep over different regularisation parameters showing that the histogram entropy is a suitable metric as it is well behaved over a wide range of parameters. The parameter determination is a feedback loop approach we applied to numerically simulated FORBILD phantom data and verified with an experimental measurement of a micro-CT device. The outcome is evaluated visually and quantitatively by means of root mean squared error (RMSE) and structural similarity (SSIM) for the simulation and visually for the measured sample (no ground truth available). The final reconstructed images exhibit noise-suppressed iterative reconstruction. For both datasets, the optimisation is robust where its initial value is concerned. The parameter tuning approach shows that the proposed metric-driven feedback loop is a promising tool for finding a suitable regularisation parameter in statistical iterative reconstruction.

Tilted grating phase-contrast computed tomography using statistical iterative reconstruction.

Grating-based phase-contrast computed tomography (GBPC-CT) enables increased soft tissue differentiation, but often suffers from streak artifacts when performing high-sensitivity GBPC-CT of biomedical samples. Current GBPC-CT setups consist of one-dimensional gratings and hence allow to measure only the differential phase-contrast (DPC) signal perpendicular to the direction of the grating lines. Having access to the full two-dimensional DPC signal can strongly reduce streak artefacts showing up as characteristic horizontal lines in the reconstructed images. GBPC-CT with gratings tilted by 45° around the optical axis, combining opposed projections, and reconstructing with filtered backprojection is one method to retrieve the full three-dimensional DPC signal. This approach improves the quality of the tomographic data as already demonstrated at a synchrotron facility. However, additional processing and interpolation is necessary, and the approach fails when dealing with cone-beam geometry setups. In this work, we employ the tilted grating configuration with a laboratory GBPC-CT setup with cone-beam geometry and use statistical iterative reconstruction (SIR) with a forward model accounting for diagonal grating alignment. Our results show a strong reduction of streak artefacts and significant increase in image quality. In contrast to the prior approach our proposed method can be used in a laboratory environment due to its cone-beam compatibility.

Myoanatomy of the velvet worm leg revealed by laboratory-based nanofocus X-ray source tomography.

X-ray computed tomography (CT) is a powerful noninvasive technique for investigating the inner structure of objects and organisms. However, the resolution of laboratory CT systems is typically limited to the micrometer range. In this paper, we present a table-top nanoCT system in conjunction with standard processing tools that is able to routinely reach resolutions down to 100 nm without using X-ray optics. We demonstrate its potential for biological investigations by imaging a walking appendage of Euperipatoides rowelli, a representative of Onychophora-an invertebrate group pivotal for understanding animal evolution. Comparative analyses proved that the nanoCT can depict the external morphology of the limb with an image quality similar to scanning electron microscopy, while simultaneously visualizing internal muscular structures at higher resolutions than confocal laser scanning microscopy. The obtained nanoCT data revealed hitherto unknown aspects of the onychophoran limb musculature, enabling the 3D reconstruction of individual muscle fibers, which was previously impossible using any laboratory-based imaging technique.

Grating-based phase-contrast and dark-field computed tomography: a single-shot method.

Grating-based X-ray interferometry offers vast potential for imaging materials and tissues that are not easily visualised using conventional X-ray imaging. Tomographic reconstruction based on X-ray interferometric data provides not only access to the attenuation coefficient of an object, but also the refractive index and information about ultra-small-angle scattering. This improved functionality comes at the cost of longer measurement times because existing projection-based signal extraction algorithms require not only a single measurement per projection angle but several with precise grating movements in between. This obstacle hinders the adaptation of grating-based interferometry into a continuously rotating gantry. Several solutions to this problem have been proposed but all suffer from major drawbacks. We present results using an iterative reconstruction algorithm working directly on the interferograms. The suggested direct approach enables improved image quality, since interpolations and unnecessary assumptions about the object are circumvented. Our results demonstrate that it is possible to successfully reconstruct the linear attenuation coefficient, the refractive index and the linear diffusion coefficient, which is a measure related to ultra-small-angle scattering, using a single measurement per projection angle and without any grating movements. This is a milestone for future clinical implementation of grating-based phase-contrast and dark-field contrast X-ray computed tomography.

Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

OBJECTIVE: Osteoporosis diagnosis using multidetector CT (MDCT) is limited to relatively high radiation exposure. We investigated the effect of simulated ultra-low-dose protocols on in-vivo bone mineral density (BMD) and quantitative trabecular bone assessment. MATERIALS AND METHODS: Institutional review board approval was obtained. Twelve subjects with osteoporotic vertebral fractures and 12 age- and gender-matched controls undergoing routine thoracic and abdominal MDCT were included (average effective dose: 10 mSv). Ultra-low radiation examinations were achieved by simulating lower tube currents and sparse samplings at 50%, 25% and 10% of the original dose. BMD and trabecular bone parameters were extracted in T10-L5. RESULTS: Except for BMD measurements in sparse sampling data, absolute values of all parameters derived from ultra-low-dose data were significantly different from those derived from original dose images (p<0.05). BMD, apparent bone fraction and trabecular thickness were still consistently lower in subjects with than in those without fractures (p<0.05). CONCLUSION: In ultra-low-dose scans, BMD and microstructure parameters were able to differentiate subjects with and without vertebral fractures, suggesting osteoporosis diagnosis is feasible. However, absolute values differed from original values. BMD from sparse sampling appeared to be more robust. This dose-dependency of parameters should be considered for future clinical use. KEY POINTS: • BMD and quantitative bone parameters are assessable in ultra-low-dose in vivo MDCT scans. • Bone mineral density does not change significantly when sparse sampling is applied. • Quantitative trabecular bone microstructure measurements are sensitive to dose reduction. • Osteoporosis subjects could be differentiated even at 10% of original dose. • Radiation exposure should be considered when comparing quantitative bone parameters.

Dentinal tubules revealed with X-ray tensor tomography.

Dentin is a mineralized material making up most of the tooth bulk. A system of microtubules, so called dentinal tubules, transverses it radially from the pulp chamber to the outside. This highly oriented structure leads to anisotropic mechanical properties directly connected to the tubules orientation and density: the ultimate tensile strength as well as the fracture toughness and the shear strength are largest perpendicular to dentinal tubules. Consequently, the fatigue strength depends on the direction of dentinal tubules, too. However, none of the existing techniques used to investigate teeth provide access to orientation and density of dentinal tubules for an entire specimen in a non-destructive way. In this paper, we measure a third molar human tooth both with conventional micro-CT and X-ray tensor tomography (XTT). While the achievable resolution in micro-CT is too low to directly resolve the dentinal tubules, we provide strong evidence that the direction and density of dentinal tubules can be indirectly measured by XTT, which exploits small-angle X-ray scattering to retrieve a 3D map of scattering tensors. We show that the mean directions of scattering structures correlate to the orientation of dentinal tubules and that the mean effective scattering strength provides an estimation of the relative density of dentinal tubules. Thus, this method could be applied to investigate the connection between tubule orientation and fatigue or tensile properties of teeth for a full sample without cutting one, non-representative peace of tooth out of the full sample.

X-ray deconvolution microscopy.

Recent advances in single-photon-counting detectors are enabling the development of novel approaches to reach micrometer-scale resolution in x-ray imaging. One example of such a technology are the MEDIPIX3RX-based detectors, such as the LAMBDA which can be operated with a small pixel size in combination with real-time on-chip charge-sharing correction. This characteristic results in a close to ideal, box-like point spread function which we made use of in this study. The proposed method is based on raster-scanning the sample with sub-pixel sized steps in front of the detector. Subsequently, a deconvolution algorithm is employed to compensate for blurring introduced by the overlap of pixels with a well defined point spread function during the raster-scanning. The presented approach utilizes standard laboratory x-ray equipment while we report resolutions close to 10 µm. The achieved resolution is shown to follow the relationship [Formula: see text] with the pixel-size p of the detector and the number of raster-scanning steps n.

Constrained X-ray tensor tomography reconstruction.

Quite recently, a method has been presented to reconstruct X-ray scattering tensors from projections obtained in a grating interferometry setup. The original publications present a rather specialised approach, for instance by suggesting a single SART-based solver. In this work, we propose a novel approach to solving the inverse problem, allowing the use of other algorithms than SART (like conjugate gradient), a faster tensor recovery, and an intuitive visualisation. Furthermore, we introduce constraint enforcement for X-ray tensor tomography (cXTT) and demonstrate that this yields visually smoother results in comparison to the state-of-art approach, similar to regularisation.

Statistical iterative reconstruction algorithm for X-ray phase-contrast CT.

Grating-based phase-contrast computed tomography (PCCT) is a promising imaging tool on the horizon for pre-clinical and clinical applications. Until now PCCT has been plagued by strong artifacts when dense materials like bones are present. In this paper, we present a new statistical iterative reconstruction algorithm which overcomes this limitation. It makes use of the fact that an X-ray interferometer provides a conventional absorption as well as a dark-field signal in addition to the phase-contrast signal. The method is based on a statistical iterative reconstruction algorithm utilizing maximum-a-posteriori principles and integrating the statistical properties of the raw data as well as information of dense objects gained from the absorption signal. Reconstruction of a pre-clinical mouse scan illustrates that artifacts caused by bones are significantly reduced and image quality is improved when employing our approach. Especially small structures, which are usually lost because of streaks, are recovered in our results. In comparison with the current state-of-the-art algorithms our approach provides significantly improved image quality with respect to quantitative and qualitative results. In summary, we expect that our new statistical iterative reconstruction method to increase the general usability of PCCT imaging for medical diagnosis apart from applications focused solely on soft tissue visualization.

X-ray nanotomography using near-field ptychography.

Propagation-based imaging or inline holography in combination with computed tomography (holotomography) is a versatile tool to access a sample's three-dimensional (3D) micro or nano structure. However, the phase retrieval step needed prior to tomographic reconstruction can be challenging especially for strongly absorbing and refracting samples. Near-field ptychography is a recently developed phase imaging method that has been proven to overcome this hurdle in projection data. In this work we extend near-field ptychography to three dimensions and we show that, in combination with tomography, it can access the nano structure of a solid oxide fuel cell (SOFC). The quality of the resulting tomographic data and the structural properties of the anode extracted from this volume were compared to previous results obtained with holotomography. This work highlights the potential of 3D near-field ptychography for reliable and detailed investigations of samples at the nanometer scale, with important applications in materials and life sciences among others.

Reduction of metal artifact in single photon-counting computed tomography by spectral-driven iterative reconstruction technique.

PURPOSE: The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR). METHOD: The proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods. RESULTS: Decomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms. CONCLUSION: It is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers.

Regularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography.

Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.