Coded aperture X-ray coherent scatter imaging is a novel modality for ascertaining the molecular structure of an object. Measurements from different spatial locations and spectral channels in the object are multiplexed through a radiopaque material (coded aperture) onto the detectors. Iterative algorithms such as penalized expectation maximization (EM) and fully separable spectrally-grouped edge-preserving reconstruction have been proposed to recover the spatially-dependent coherent scatter spectral image from the multiplexed measurements. Such image recovery methods fall into the category of domain decomposition methods since they recover independent pieces of the image at a time. Ordered subsets has also been utilized in conjunction with penalized EM to accelerate its convergence. Ordered subsets is a range decomposition method because it uses parts of the measurements at a time to recover the image. In this paper, we analyze domain and range decomposition methods as they apply to coded aperture X-ray coherent scatter imaging using a spectrally-grouped edge-preserving regularizer and discuss the implications of the increased availability of parallel computational architecture on the choice of decomposition methods. We present results of applying the decomposition methods on experimental coded aperture X-ray coherent scatter measurements. Based on the results, an underlying observation is that updating different parts of the image or using different parts of the measurements in parallel, decreases the rate of convergence, whereas using the parts sequentially can accelerate the rate of convergence.
Previous realizations of coded-aperture X-ray diffraction tomography (XRDT) techniques based on pencil beams image one line through an object via a single measurement but require raster scanning the object in multiple dimensions. Fan beam approaches are able to image the spatial extent of the object while retaining the ability to do material identification. Building on these approaches we present our system concept and geometry of combining a fan beam with energy sensitive/photon counting detectors and a coded aperture to capture both spatial and spectral information about an object at each voxel. Using our system we image slices via snapshot measurements for four different detector configurations and compare their results.
We use coded apertures and multiple views to create a compressive coherent scatter computed tomography (CSCT) system. Compared with other CSCT systems, we reduce object dose and scan time. Previous work on coded aperture tomography resulted in a resolution anisotropy that caused poor or unusable momentum transfer resolution in certain cases. Complimentary and multiple views resolve the resolution issues, while still providing the ability to perform snapshot tomography by adding sources and detectors.
Coded aperture X-ray diffraction (coherent scatter spectral) imaging provides fast and dose-efficient measurements of the molecular structure of an object. The information provided is spatially-dependent and material-specific, and can be utilized in medical applications requiring material discrimination, such as tumor imaging.
However, current coded aperture coherent scatter spectral imaging system assume a uniformly or weakly attenuating object, and are plagued by image degradation due to non-uniform self-attenuation. We propose accounting
for such non-uniformities in the self-attenuation by utilizing an X-ray computed tomography (CT) image (reconstructed attenuation map). In particular, we present an iterative algorithm for coherent scatter spectral image
reconstruction, which incorporates the attenuation map, at different stages, resulting in more accurate coherent
scatter spectral images in comparison to their uncorrected counterpart. The algorithm is based on a spectrally
grouped edge-preserving regularizer, where the neighborhood edge weights are determined by spatial distances
and attenuation values.
KEYWORDS: Sensors, X-rays, Coded apertures, Fluctuations and noise, Tomography, 3D image reconstruction, Scattering, Signal detection, 3D image processing, X-ray detectors
We present a system for X-ray tomography using a coded aperture. A fan beam illuminates a 2D cross-section
of an object and our coded aperture system produces a tomographic image from each static snapshot; as such,
we can reconstruct either a static object scanned in 3D or an x-ray video of a non-static object.
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