Purpose
Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach

Method
We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise In addition, our CNN is designed with a residual learning architecture for faster network training and better performance

Results
Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 “Low-Dose CT Grand Challenge”

Conclusions
To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.12344

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the usual bulk, macroscopic parameters used to characterize soils (e.g., granulometry, pH, soil organic matter and biomass contents) provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gases. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale). For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. In terms of microbial aspects, whereas a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because relevant experimental data are extremely scarce. For the overall research to move forward, it will be crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead.

/pdf/emergent-properties-of-microbial-activity-in-heterogeneous-29gqm4n055.pdf

Emergent Properties of Microbial Activity in Heterogeneous Soil Microenvironments: Different Research Approaches Are Slowly Converging, Yet Major Challenges Remain.

The mechanical properties of many materials are based on the macroscopic arrangement and orientation of their nanostructure. This nanostructure can be ordered over a range of length scales. In biology, the principle of hierarchical ordering is often used to maximize functionality, such as strength and robustness of the material, while minimizing weight and energy cost. Methods for nanoscale imaging provide direct visual access to the ultrastructure (nanoscale structure that is too small to be imaged using light microscopy), but the field of view is limited and does not easily allow a full correlative study of changes in the ultrastructure over a macroscopic sample. Other methods of probing ultrastructure ordering, such as small-angle scattering of X-rays or neutrons, can be applied to macroscopic samples; however, these scattering methods remain constrained to two-dimensional specimens or to isotropically oriented ultrastructures. These constraints limit the use of these methods for studying nanostructures with more complex orientation patterns, which are abundant in nature and materials science. Here, we introduce an imaging method that combines small-angle scattering with tensor tomography to probe nanoscale structures in three-dimensional macroscopic samples in a non-destructive way. We demonstrate the method by measuring the main orientation and the degree of orientation of nanoscale mineralized collagen fibrils in a human trabecula bone sample with a spatial resolution of 25 micrometres. Symmetries within the sample, such as the cylindrical symmetry commonly observed for mineralized collagen fibrils in bone, allow for tractable sampling requirements and numerical efficiency. Small-angle scattering tensor tomography is applicable to both biological and materials science specimens, and may be useful for understanding and characterizing smart or bio-inspired materials. Moreover, because the method is non-destructive, it is appropriate for in situ measurements and allows, for example, the role of ultrastructure in the mechanical response of a biological tissue or manufactured material to be studied.

/pdf/nanostructure-surveys-of-macroscopic-specimens-by-small-3rv3wgrl88.pdf

Nanostructure surveys of macroscopic specimens by small-angle scattering tensor tomography

Due to the potential risk of inducing cancers, radiation dose of X-ray CT should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts usually occur due to photon starvation, beamhardening, etc, which decrease the reliability of diagnosis. Thus, high quality reconstruction from low-dose X-ray CT data has become one of the important research topics in CT community. Conventional model-based denoising approaches are, however, computationally very expensive, and image domain denoising approaches hardly deal with CT specific noise patterns. To address these issues, we propose an algorithm using a deep convolutional neural network (CNN), which is applied to wavelet transform coefficients of low-dose CT images. Specifically, by using a directional wavelet transform for extracting directional component of artifacts and exploiting the intra- and inter-band correlations, our deep network can effectively suppress CT specific noises. Moreover, our CNN is designed to have various types of residual learning architecture for faster network training and better denoising. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns of CT images, originated from the reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient in removing the noises from low-dose CT compared to an image domain CNN. Our results were rigorously evaluated by several radiologists and won the second place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our knowledge, this work is the first deep learning architecture for low-dose CT reconstruction that has been rigorously evaluated and proven for its efficacy.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction

Fast laboratory-based micro-computed tomography for pore-scale research: Illustrative experiments and perspectives on the future

While large-scale synchrotron sources provide a highly brilliant monochromatic X-ray beam, these X-ray sources are expensive in terms of installation and maintenance, and require large amounts of space due to the size of storage rings for GeV electrons. On the other hand, laboratory X-ray tube sources can easily be implemented in laboratories or hospitals with comparatively little cost, but their performance features a lower brilliance and a polychromatic spectrum creates problems with beam hardening artifacts for imaging experiments. Over the last decade, compact synchrotron sources based on inverse Compton scattering have evolved as one of the most promising types of laboratory-scale X-ray sources: they provide a performance and brilliance that lie in between those of large-scale synchrotron sources and X-ray tube sources, with significantly reduced financial and spatial requirements. These sources produce X-rays through the collision of relativistic electrons with infrared laser photons. In this study, an analysis of the performance, such as X-ray flux, source size and spectra, of the first commercially sold compact light source, the Munich Compact Light Source, is presented.

The Munich Compact Light Source: initial performance measures.

Between X-ray tubes and large-scale synchrotron sources, a large gap in performance exists with respect to the monochromaticity and brilliance of the X-ray beam. However, due to their size and cost, large-scale synchrotrons are not available for more routine applications in small and medium-sized academic or industrial laboratories. This gap could be closed by laser-driven compact synchrotron light sources (CLS), which use an infrared (IR) laser cavity in combination with a small electron storage ring. Hard X-rays are produced through the process of inverse Compton scattering upon the intersection of the electron bunch with the focused laser beam. The produced X-ray beam is intrinsically monochromatic and highly collimated. This makes a CLS well-suited for applications of more advanced--and more challenging--X-ray imaging approaches, such as X-ray multimodal tomography. Here we present, to our knowledge, the first results of a first successful demonstration experiment in which a monochromatic X-ray beam from a CLS was used for multimodal, i.e., phase-, dark-field, and attenuation-contrast, X-ray tomography. We show results from a fluid phantom with different liquids and a biomedical application example in the form of a multimodal CT scan of a small animal (mouse, ex vivo). The results highlight particularly that quantitative multimodal CT has become feasible with laser-driven CLS, and that the results outperform more conventional approaches.

https://www.pnas.org/content/pnas/112/18/5567.full.pdf

X-ray phase-contrast tomography with a compact laser-driven synchrotron source

Here we introduce a new concept for x-ray computed tomography that yields information about the local micro-morphology and its orientation in each voxel of the reconstructed 3D tomogram. Contrary to conventional x-ray CT, which only reconstructs a single scalar value for each point in the 3D image, our approach provides a full scattering tensor with multiple independent structural parameters in each volume element. In the application example shown in this study, we highlight that our method can visualize sub-pixel fiber orientations in a carbon composite sample, hence demonstrating its value for non-destructive testing applications. Moreover, as the method is based on the use of a conventional x-ray tube, we believe that it will also have a great impact in the wider range of material science investigations and in future medical diagnostics.

https://iopscience.iop.org/article/10.1209/0295-5075/105/38002/pdf

X-ray tensor tomography

Besides the overall mass density, strength of trabecular bone depends significantly on its microstructure. However, due to dose constraints in medical CT imaging, it is impossible to gain sufficient information about very fine bone structures in vivo on the micrometer scale. Here we show that a recently developed method of X-ray vector radiography (XVR), an imaging method which uses X-ray scattering information to form an image, allows predictions on the bone microstructure without the explicit need to spatially resolve even individual trabeculae in the bone. We investigated thick human femoral bone samples and compared state-of-the-art μCT data with XVR imaging. A model is presented which proves that XVR imaging yields information directly correlated with the trabecular microstructure. This opens up possibilities of using XVR as a tool to help early diagnosis of bone diseases, such as osteoporosis.

/pdf/correlation-of-x-ray-vector-radiography-to-bone-micro-3y5gk15fsq.pdf

Correlation of X-Ray Vector Radiography to Bone Micro-Architecture

Inverse Compton scattering provides means to generate low-divergence partially coherent quasi-monochromatic, i.e. synchrotron-like, X-ray radiation on a laboratory scale. This enables the transfer of synchrotron techniques into university or industrial environments. Here, the Munich Compact Light Source is presented, which is such a compact synchrotron radiation facility based on an inverse Compton X-ray source (ICS). The recent improvements of the ICS are reported first and then the various experimental techniques which are most suited to the ICS installed at the Technical University of Munich are reviewed. For the latter, a multipurpose X-ray application beamline with two end-stations was designed. The beamline's design and geometry are presented in detail including the different set-ups as well as the available detector options. Application examples of the classes of experiments that can be performed are summarized afterwards. Among them are dynamic in vivo respiratory imaging, propagation-based phase-contrast imaging, grating-based phase-contrast imaging, X-ray microtomography, K-edge subtraction imaging and X-ray spectroscopy. Finally, plans to upgrade the beamline in order to enhance its capabilities are discussed.

/pdf/the-versatile-x-ray-beamline-of-the-munich-compact-light-nhxu55bqby.pdf

Elena Eggl

Papers

The Munich Compact Light Source: initial performance measures.

X-ray phase-contrast tomography with a compact laser-driven synchrotron source

X-ray tensor tomography

Correlation of X-Ray Vector Radiography to Bone Micro-Architecture

The versatile X-ray beamline of the Munich Compact Light Source: design, instrumentation and applications.