Edge illumination (EI) is an x-ray phase-contrast imaging technique, exploiting sensitivity to x-ray refraction to visualize features, which are often not detected by conventional absorption-based radiography. The method does not require a high degree of spatial coherence and is achromatic and, therefore, can be implemented with both synchrotron radiation and commercial x-ray tubes. Using different retrieval algorithms, information about an object’s attenuation, refraction, and scattering properties can be obtained. In recent years, a theoretical framework has been developed that enables EI computed tomography (CT) and, hence, three-dimensional imaging. This review provides a summary of these advances, covering the development of different image acquisition schemes, retrieval approaches, and applications. These developments constitute an integral part in the transformation of EI CT into a widely spread imaging tool for use in a range of fields.
The implementation of X-Ray Phase Contrast (XPC) imaging at synchrotrons has demonstrated transformative potential on a wide range of applications, from medicine and biology to materials science. However, translation to conventional laboratory sources has proven more problematic, because of XPC’s stringent requirements in terms of spatial coherence. This has imposed the use of either micro-focal sources, or collimators (e.g. source gratings) where sources with extended focal spots were used. This reduces the available x-ray flux leading to long exposure times, which is often exacerbated by the use of additional optical elements that need to be scanned during image acquisition. Where these elements are placed downstream of the object, they also lead to an increase in the delivered dose.
XPC has also been successfully adapted to full 3D, computed tomography (CT) implementations, which has however exacerbated the above concerns in terms of acquisition times and delivered doses.
We tackled this problem by developing an incoherent approach to XPC that works with non micro-focal laboratory sources without requiring any additional collimation. The method uses one or two low aspect ratio x-ray masks that are built on low-absorbing graphite substrates for maximum transmission through the mask apertures. The combination of this with a “single-shot” phase retrieval algorithm has enabled the development of a lab-based XPC-CT system that can perform a full scan in a few minutes while delivering low radiation doses. The talk will briefly describe how the method works, then show application examples including direct comparisons with the synchrotron gold standard.
In this article we discuss three different developments in Edge Illumination (EI) X-ray phase contrast imaging
(XPCi), all ultimately aimed at optimising EI computed tomography (CT) for use in different environments, and
for different applications. For the purpose of reducing scan times, two approaches are presented; the reverse
projection" acquisition scheme which allows a continuous rotation of the sample, and the single image" retrieval
algorithm, which requires only one frame for retrieval of the projected phase map. These are expected to lead
to a substantial reduction of EI CT scan times, a prospect which is likely to promote the translation of EI into
several applications, including clinical. The last development presented is the "modified local" phase retrieval.
This retrieval algorithm is specifically designed to accurately retrieve sample properties (absorption, refraction,
scattering) in cases where high-resolution scans are required in non-ideal environments. Experimental results,
using both synchrotron radiation and laboratory sources, are shown for the various approaches.
The application of x-ray phase contrast computed tomography (PCT) to the field of tissue engineering is dis- cussed. Specific focus is on the edge illumination PCT method, which can be adapted to weakly coherent x-ray sources, permitting PCT imaging in standard (non-synchrotron) laboratory environments. The method was applied to a prominent research topic in tissue engineering, namely the development of effective and reliable decellularization protocols to derive scaffolds from native tissue. Results show that edge illumination PCT provides sufficient image quality to evaluate the microstructural integrity of scaffolds and, thus, to assess the performance of the used decellularization technique. In order to highlight that edge illumination PCT can ultimately comply with demands on a high specimen throughput and low doses of radiation, recently developed strategies for scan time and dose reduction are discussed.
Phase contrast X-ray computed tomography (PCI-CT) has recently emerged as a novel imaging technique that allows visualization of cartilage soft tissue, subsequent examination of chondrocyte patterns, and their correlation to osteoarthritis. Previous studies have shown that 2D texture features are effective at distinguishing between healthy and osteoarthritic regions of interest annotated in the radial zone of cartilage matrix on PCI-CT images. In this study, we further extend the texture analysis to 3D and investigate the ability of volumetric texture features at characterizing chondrocyte patterns in the cartilage matrix for purposes of classification. Here, we extracted volumetric texture features derived from Minkowski Functionals and gray-level co-occurrence matrices (GLCM) from 496 volumes of interest (VOI) annotated on PCI-CT images of human patellar cartilage specimens. The extracted features were then used in a machine-learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with GLCM features correlation (AUC = 0.83 ± 0.06) and homogeneity (AUC = 0.82 ± 0.07), which significantly outperformed all Minkowski Functionals (p < 0.05). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving GLCM-derived statistical features can distinguish between healthy and osteoarthritic tissue with high accuracy.
Edge illumination (EI) X-ray phase-contrast imaging (XPCI) has potential for applications in different fields of research, including materials science, non-destructive industrial testing, small-animal imaging, and medical imaging. One of its main advantages is the compatibility with laboratory equipment, in particular with conventional non-microfocal sources, which makes its exploitation in normal research laboratories possible. In this work, we demonstrate that the signal in laboratory implementations of EI can be correctly described with the use of the simplified geometrical optics. Besides enabling the derivation of simple expressions for the sensitivity and spatial resolution of a given EI setup, this model also highlights the EI’s achromaticity. With the aim of improving image quality, as well as to take advantage of the fact that all energies in the spectrum contribute to the image contrast, we carried out EI acquisitions using a photon-counting energy-resolved detector. The obtained results demonstrate that this approach has great potential for future laboratory implementations of EI.
This article discusses two experimental setups of edge illumination (EI) x-ray phase contrast imaging (XPCi) as well as
the theory that is required to reconstruct quantitative tomographic maps using established methods, e.g. filtered back
projection (FBP). Tomographic EI XPCi provides the option to reconstruct volumetric maps of different physical
quantities, amongst which are the refractive index decrement from unity and the absorption coefficient, which can be
used for dual-mode imaging. EI XPCi scans of a custom-built wire phantom using synchrotron and x-ray tube generated
radiation were carried out, and tomographic maps of both parameters were reconstructed. This article further discusses
the theoretical basis for the tomographic reconstruction of images showing combined phase and attenuation contrast.
Corresponding experimental results are presented.
We present a development of the laboratory-based implementation of edge-illumination (EI) x-ray phase contrast
imaging (XPCI) that simultaneously enables low-dose and high sensitivity. Lab-based EI-XPCI simplifies the set-up
with respect to other methods, as it only requires two optical elements, the large pitch of which relaxes the alignment
requirements. Albeit in the past it was erroneously assumed that this would reduce the sensitivity, we demonstrate
quantitatively that this is not the case.
We discuss a system where the pre-sample mask open fraction is smaller than 50%, and a large fraction of the created
beamlets hits the apertures in the detector mask. This ensures that the majority of photons traversing the sample are
detected i.e. used for image formation, optimizing dose delivery. We show that the sensitivity depends on the dimension
of the part of each beamlet hitting the detector apertures, optimized in the system design. We also show that the aperture
pitch does not influence the sensitivity. Compared to previous implementations, we only reduced the beamlet fraction
hitting the absorbing septa on the detector mask, not the one falling inside the apertures: the same number of x-rays per
second is thus detected, i.e. the dose is reduced, but not at the expense of exposure time.
We also present an extension of our phase-retrieval algorithm enabling the extraction of ultra-small-angle scattering by
means of only one additional frame, with all three frames acquired within dose limits imposed by e.g. clinical
mammography, and easy adaptation to lab-based phase-contrast x-ray microscopy implementations.
Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.
The current approach to evaluating cartilage degeneration at the knee joint requires visualization of the joint space on
radiographic images where indirect cues such as joint space narrowing serve as markers for osteoarthritis. A recent novel approach to visualizing the knee cartilage matrix using phase contrast imaging (PCI) with computed tomography (CT) was shown to allow direct examination of chondrocyte patterns and their subsequent correlation to osteoarthritis. This study aims to characterize chondrocyte cell patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage through texture analysis. Statistical features derived from gray-level co-occurrence matrices (GLCM) and geometric features derived from the Scaling Index Method (SIM) were extracted from 404 regions of interest (ROI) annotated on PCI images of healthy and osteoarthritic specimens of knee cartilage. These texture features were then used in a machine learning task to classify ROIs as healthy or osteoarthritic. A fuzzy k-nearest neighbor classifier was used and its performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional
geometrical feature vectors derived from SIM and GLCM correlation features. With the experimental conditions used in
this study, both SIM and GLCM achieved a high classification performance (AUC value of 0.98) in the task of distinguishing between healthy and osteoarthritic ROIs. These results show that such quantitative analysis of
chondrocyte patterns in the knee cartilage matrix can distinguish between healthy and osteoarthritic tissue with high
accuracy.
The edge illumination principle was first proposed at Elettra (Italy) in the late nineties, as an alternative method
for achieving high phase sensitivity with a very simple and flexible set-up, and has since been under continuous
development in the radiation physics group at UCL. Edge illumination allows overcoming most of the limitations
of other phase-contrast techniques, enabling their translation into a laboratory environment. It is relatively
insensitive to mechanical and thermal instabilities and it can be adapted to the divergent and polychromatic
beams provided by X-ray tubes. This method has been demonstrated to work efficiently with source sizes up
to 100m, compatible with state-of-the-art mammography sources. Two full prototypes have been built and
are operational at UCL. Recent activity focused on applications such as breast and cartilage imaging, homeland
security and detection of defects in composite materials. New methods such as phase retrieval, tomosynthesis
and computed tomography algorithms are currently being theoretically and experimentally investigated. These
results strongly indicate the technique as an extremely powerful and versatile tool for X-ray imaging in a wide
range of applications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.