Recent advances in edge illumination x-ray phase-contrast tomography

Joint multi-contrast CT for edge illumination X-ray phase contrast imaging using split Barzilai-Borwein steps

Edge illumination (EI) is an X-ray imaging technique that, in addition to conventional absorption contrast, provides refraction and scatter contrast. It relies on an absorption mask in front of the sample that splits the X-ray beam into beamlets, which hits a second absorption mask positioned in front of the detector. The sample mask is then shifted in multiple steps with respect to the detector mask, thereby measuring an illumination curve per detector element. The width, position, and area of this curve estimated with and without the sample in the beam is then compared, which ultimately provides absorption, refraction, and scatter contrast for each detector pixel. From the obtained contrast sinograms, three contrast tomograms can be computed. In summary, conventional EI relies on a two-stage process comprised of a computational and time intensive contrast retrieval process, followed by tomographic reconstruction. In this work, a novel joint reconstruction method is proposed, which utilizes a combined forward model to reconstruct the three contrasts simultaneously, without the need for an intermediate contrast retrieval step. Compared to the state-of-the-art, this approach reduces reconstruction times, as the retrieval step is skipped and allows a much more flexible acquisition scheme, as there is no need to sample a full illumination curve at each projection angle. The proposed method is shown to improve reconstruction quality on subsampled datasets, enabling the reconstruction of three contrasts from single-shot datasets.

An In-House Cone-Beam Tomographic Reconstruction Package for Laboratory X-ray Phase-Contrast Imaging

Phase-contrast, and in general, multi-modal, X-ray micro-tomography is proven to be very useful for low-density, low-attention samples enabling much better contrast than its attenuation-based pendant. Therefore, it is increasingly applied in bio- and life sciences primarily dealing with such samples. Although there is a plethora of literature regarding phase-retrieval algorithms, access to implementations of those algorithms is relatively limited and very few packages combining phase-retrieval methods with the full tomographic reconstruction pipeline are available. This is especially the case for laboratory-based phase-contrast imaging typically featuring cone-beam geometry. We present here an in-house cone-beam tomographic reconstruction package for laboratory X-ray phase-contrast imaging. It covers different phase-contrast techniques and phase retrieval methods. The paper explains their implementation and integration in the filtered back projection chain. Their functionality and efficiency will be demonstrated through applications on a few dedicated samples.

Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data

In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose.

Fluence adaptation for contrast-based dose optimization in x-ray phase-contrast imaging

PURPOSE

X-ray phase-contrast imaging (XPCI) can provide multiple contrasts with great potentials for clinical and industrial applications, including conventional attenuation, phase contrast, and dark field. Grating-based imaging (GBI) and edge-illumination (EI) are two promising types of XPCI as the conventional x-ray sources can be directly utilized. For the GBI and EI systems, the phase-stepping acquisition with multiple exposures at a constant fluence is usually adopted in the literature.This work, however, attempts to challenge such a constant fluence concept during the phase-stepping process and proposes a fluence adaptation mechanism for dose reduction.

METHOD

Given the importance of patient radiation dose for clinical applications, numerous studies have tried to reduce patient dose in XPCI by altering imaging system designs, data acquisition, and information retrieval. Recently, analytic multiorder moment analysis has been proposed to improve the computing efficiency. In these algorithms, multiple contrasts can be calculated by summing together the weighted phase-stepping curves (PSCs) with some kernel functions, which suggests us that the raw data at different steps have different contributions for the noise in retrieved contrasts. Therefore, it is possible to improve the noise performance by adjusting the fluence distribution during the phase-stepping process directly. Based on analytic retrieval formulas and the Gaussian noise model for detected signals, we derived an optimal adaptive fluence distribution, which is proportional to the absolute weighting kernel functions and the root of original sample PSCs acquired under the constant fluence. Considering that the original sample PSC might be unavailable, we proposed two practical forms for the GBI and EI systems, which are also able to reduce the contrast noise when comparing with the constant fluence distribution. Since the kernel functions are target contrast-dependent, our proposed fluence adaptation mechanism provides a way of realizing a contrast-based dose optimization while keeping the same noise level.

RESULTS

To validate our analyses, simulations and experiments are conducted for the GBI and EI systems. Simulated results demonstrate that the dose reduction ratio between our proposed fluence distributions and the typical constant one can be about 20% for the phase contrast, which is consistent with our theoretical predictions. Although the experimental noise reduction ratios are a little smaller than the theoretical ones, low-dose experiments observe better noise performance by our proposed method. Our simulated results also give out the effective ranges of the parameters of the PSCs, such as the visibility in the GBI, the standard deviation, and the mean value in the EI, providing a guidance for the use of our proposed approach in practice.

CONCLUSIONS

In this paper, we propose a fluence adaptation mechanism for contrast-based dose optimization in XPCI, which can be applied to the GBI and EI systems. Our proposed method explores a new direction for dose reduction, and may also be further extended to other types of XPCI systems and information retrieval algorithms.

Principles of Different X-ray Phase-Contrast Imaging: A Review

Numerous advances have been made in X-ray technology in recent years. X-ray imaging plays an important role in the nondestructive exploration of the internal structures of objects. However, the contrast of X-ray absorption images remains low, especially for materials with low atomic numbers, such as biological samples. X-ray phase-contrast images have an intrinsically higher contrast than absorption images. In this review, the principles, milestones, and recent progress of X-ray phase-contrast imaging methods are demonstrated. In addition, prospective applications are presented.

X-ray Dark-Field Imaging (XDFI)-a Promising Tool for 3D Virtual Histopathology

X-ray dark-field imaging (XDFI) utilizing a thin silicon crystal under Laue case enables visualizing three-dimensional (3D) morphological alterations of human tissue. XDFI uses refraction-contrast derived from phase shift rather than absorption as the main X-ray image contrast source to render 2D and 3D images of tissue specimens in unprecedented detail. The unique features of XDFI are its extremely high sensitivity (approximately 1000:1 compared to absorption for soft tissues under X-ray energy of around 20 keV, theoretically) and excellent resolution (8.5 μm) without requiring contrast medium or staining. Thus, XDFI-computed tomography can generate 3D virtual histological images equivalent to those of stained histological sections pathologists observe under low-power light microscopy as far as organs and tissues selected as samples in preliminary studies. This paper reviews the fundamental principles and the potential of XDFI, describes two optical setups for XDFI with examples, illustrates features of XDFI that are salient for histopathology, and presents XDFI examples of refraction-contrast images of atherosclerotic plaques, musculoskeletal tissue, neuronal tissue, and breast cancer specimens. Availability of this X-ray imaging in routine histopathological evaluations of tissue specimens would help guide clinical decision making by highlighting suspicious areas in unstained, thick sections for further sampling and analysis using conventional histopathological techniques. XDFI is a promising tool for 3D virtual histopathology.

A partial-dithering strategy for edge-illumination x-ray phase-contrast tomography enabled by a joint reconstruction method

Edge-illumination x-ray phase-contrast tomography (EIXPCT) is a promising imaging technology where partially opaque masks are utilized with laboratory-based x-ray sources to estimate the distribution of the complex-valued refractive index. EIXPCT resolution is mainly determined by the period of a sample mask, but can be significantly improved by a dithering technique. Here, dithering means that multiple images per tomographic view angle are acquired as the object is moved over sub-pixel distances. Drawbacks of dithering include increased data-acquisition times and radiation doses. Motivated by the flexibility in data-acquisition designs enabled by a recently developed joint reconstruction method, a novel partial-dithering strategy for EIXPCT data-acquisition is proposed. In this strategy, dithering is implemented at only a subset of the tomographic view angles. The strategy can result in spatial resolution comparable to that of the conventional full-dithering strategy, where dithering is performed at every view angle, but the acquisition time is substantially decreased. Here, the effect of dithering parameters on image resolution is explored.

Comparison of data-acquisition designs for single-shot edge-illumination X-ray phase-contrast tomography

Edge-illumination X-ray phase-contrast tomography (EIXPCT) is an emerging technique that enables practical phase-contrast imaging with laboratory-based X-ray sources. A joint reconstruction method was proposed for reconstructing EIXPCT images, enabling novel flexible data-acquisition designs. However, only limited efforts have been devoted to optimizing data-acquisition designs for use with the joint reconstruction method. In this study, several promising designs are introduced, such as the constant aperture position (CAP) strategy and the alternating aperture position (AAP) strategy covering different angular ranges. In computer-simulation studies, these designs are analyzed and compared. Experimental data are employed to test the designs in real-world applications. All candidate designs are also compared for their implementation complexity. The tradeoff between data-acquisition time and image quality is discussed.

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

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the bulk, macroscopic parameters (e.g., granulometry, pH, soil organic matter, and biomass contents) commonly used to characterize soils provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gasses. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale) that is commensurate with the habitat of many microorganisms. 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. With regard to the microbial aspects, although 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 of the scarcity of relevant experimental data. For significant progress to be made, it is 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. Fortunately, a number of these challenges may be resolved by brand new measuring equipment that will become commercially available in the very near future.

3D printing from microfocus computed tomography (micro-CT) in human specimens: education and future implications

Microfocus CT (micro-CT) is an imaging method that provides three-dimensional digital data sets with comparable resolution to light microscopy. Although it has traditionally been used for non-destructive testing in engineering, aerospace industries and in preclinical animal studies, new applications are rapidly becoming available in the clinical setting including post-mortem fetal imaging and pathological specimen analysis. Printing three-dimensional models from imaging data sets for educational purposes is well established in the medical literature, but typically using low resolution (0.7 mm voxel size) data acquired from CT or MR examinations. With higher resolution imaging (voxel sizes below 1 micron, <0.001 mm) at micro-CT, smaller structures can be better characterised, and data sets post-processed to create accurate anatomical models for review and handling. In this review, we provide examples of how three-dimensional printing of micro-CT imaged specimens can provide insight into craniofacial surgical applications, developmental cardiac anatomy, placental imaging, archaeological remains and high-resolution bone imaging. We conclude with other potential future usages of this emerging technique.

Properties of a Joint Reconstruction Method for Edge-Illumination X-Ray Phase-Contrast Tomography

Edge illumination X-ray phase-contrast tomography (EIXPCT) is an emerging technique for estimating the complex-valued refractive index distribution of an imaged object. A conventional EIXPCT system requires measurement of multiple images at each tomographic view angle, leading to prolonged data acquisition times. Recently, a joint reconstruction (JR) method has been developed to enable single-shot EIXPCT imaging without restrictive assumptions related to the object, imaging geometry or hardware. In the JR method, estimates of the refractive index distribution are recast as the solution to a nonlinear optimization problem. While a preliminary study has demonstrated the potential usefulness of the JR method, its numerical properties remain largely unexplored. In this project, the convexity, cross-talk properties and noise properties of the JR method are investigated.