Edge illumination X-ray phase-contrast tomography (EIXPCT) is a emerging imaging technology in which partially opaque gratings are utilized with laboratory-based X-ray sources to estimate the distribution of the complex-valued refractive index. Spatial resolution in EIXPCT is mainly determined by the grating period of a sample mask, but can be significantly improved by a dithering technique in which multiple projection images are required per tomographic view angle 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 (JR) method, a novel partial-dithering strategy for data acquisition is proposed. In this strategy, dithering is implemented at only a subset of the tomographic view angles. This results in spatial resolution that is comparable to that of the conventional full-dithering strategy where dithering is performed at every view angle, but the acquisition time is substantially decreased. The effect of dithering parameters on image resolution is explored.
X-ray phase-contrast imaging methods exploit variations in an object’s 3D refractive index distribution to form projection or volumetric images of weakly absorbing objects. Such techniques can resolve subtle tissue structures by employing coherent imaging principles, but retain the ability of traditional (incoherent) X-ray methods to image deep into tissue. In this talk, we describe recent advancements in image formation methods for benchtop applications of X-ray phase-contrast imaging and tomography and present applications of pre-clinical in vivo imaging.
Edge illumination X-ray phase-contrast tomography (EIXPCT) is an emerging imaging technology capable of estimating the complex-valued refractive index distribution with laboratory-based X-ray sources. Conventional image reconstruction approaches for EIXPCT require multiple images to be acquired at each tomographic view angle. This contributes to prolonged data-acquisition times and potentially elevated radiation doses, which can hinder in-vivo applications. A new “single-shot” method has been proposed for joint reconstruction (JR) of the real and imaginary-valued components of the refractive index distribution from a tomographic data set that contains only a single image acquired at each view angle. The JR method does not place restrictions on the types of measurement data that it can be applied to and therefore enables the exploration of innovation single-shot data-acquisition designs. However, there remains an important need to identify data-acquisition designs that will permit accurate JR. In this study, innovative, JR-enabled, single-shot data-acquisition designs for EIXPCT are proposed and characterized quantitatively in simulation studies.
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.
Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.
KEYWORDS: Magnetic resonance imaging, Bayesian inference, Image analysis, Reconstruction algorithms, Image restoration, Probability theory, Medical image reconstruction, Monte Carlo methods, Reliability, Optical imaging, Brain mapping, Imaging systems, Medical imaging, Brain, Image segmentation, Data modeling
Point estimates, such as the maximum a posteriori (MAP) estimate, are commonly computed in image re-
construction tasks. However, such point estimates provide no information about the range of highly probable
solutions, namely the uncertainty in the computed estimate. Bayesian inference methods that seek to compute
the posterior probability distribution function (PDF) of the object can provide exactly this information, but
are generally computationally intractable. Markov Chain Monte Carlo (MCMC) methods, which avoid explicit
posterior computation by directly sampling from the PDF, require considerable expertise to run in a proper
way. This work investigates a computationally efficient variational Bayesian inference approach for computing
the posterior image variance with application to MRI. The methodology employs a sparse object prior model
that is consistent with the model assumed in most sparse reconstruction methods. The posterior variance map
generated by the proposed method provides valuable information that reveals how data-acquisition parameters
and the specification of the object prior affect the reliability of a reconstructed MAP image. The proposed
method is demonstrated by use of computer-simulated MRI data.
Edge illumination X-ray phase-contrast tomography (EIXPCT) is an imaging technique that estimates the spatially variant X-ray refractive index and absorption distribution within an object while seeking to circumvent the limitations of previous benchtop implementations of X-ray phase-contrast tomography. As with gratingor analyzer-based methods, conventional image reconstruction methods for EIXPCT require that two or more images be acquired at each tomographic view angle. This requirement leads to increased data acquisition times, hindering in vivo applications. To circumvent these limitations, a joint reconstruction (JR) approach is proposed that concurrently produces estimates of the refractive index and absorption distributions from a tomographic data set containing only a single image per tomographic view angle. The JR reconstruction method solves a nonlinear optimization problem by use of a novel iterative gradient-based algorithm. The JR method is demonstrated in both computer-simulated and experimental EIXPCT studies.
We present single-shot real-time video recording of light scattering dynamics by second-generation compressed ultrafast photography (G2-CUP). Using G2-CUP at 100 billion frames per second, in a single camera exposure, we experimentally captured the evolution of the light intensity distribution in an engineered thin scattering plate assembly. G2-CUP, which implements a new reconstruction paradigm and a more efficient hardware design than its predecessors, markedly improves the reconstructed image quality. The ultrafast imaging reveals the instantaneous light scattering pattern as a photonic Mach cone. We envision that our technology will find a diverse range of applications in biomedical imaging, materials science, and physics.
Propagation-based X-ray phase-contrast tomography (XPCT) provides the opportunity to image weakly absorbing objects and is being explored actively for a variety of important pre-clinical applications. Quantitative XPCT image reconstruction methods typically involve a phase retrieval step followed by application of an image reconstruction algorithm. Most approaches to phase retrieval require either acquiring multiple images at different object-to-detector distances or introducing simplifying assumptions, such as a single-material assumption, to linearize the imaging model. In order to overcome these limitations, a non-linear image reconstruction method has been proposed previously that jointly estimates the absorption and refractive properties of an object from XPCT projection data acquired at a single propagation distance, without the need to linearize the imaging model. However, the numerical properties of the associated non-convex optimization problem remain largely unexplored. In this study, computer simulations are conducted to investigate the feasibility of the joint reconstruction problem in practice. We demonstrate that the joint reconstruction problem is ill-posed and sensitive to system inconsistencies. Particularly, the method can generate accurate refractive index images only if the object is thin and has no phase-wrapping in the data. However, we also observed that, for weakly absorbing objects, the refractive index images reconstructed by the joint reconstruction method are, in general, more accurate than those reconstructed using methods that simply ignore the object’s absorption.
The single-shot compressed ultrafast photography (CUP) camera is the fastest receive-only camera in the world. In this work, we introduce an external CCD camera and a space- and intensity-constrained (SIC) reconstruction algorithm to improve the image quality of CUP. The CCD camera takes a time-unsheared image of the dynamic scene. Unlike the previously used unconstrained algorithm, the proposed algorithm incorporates both spatial and intensity constraints, based on the additional prior information provided by the external CCD camera. First, a spatial mask is extracted from the time-unsheared image to define the zone of action. Second, an intensity threshold constraint is determined based on the similarity between the temporally projected image of the reconstructed datacube and the time-unsheared image taken by the external CCD. Both simulation and experimental studies showed that the SIC reconstruction improves the spatial resolution, contrast, and general quality of the reconstructed image.
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