X-Ray Computed Tomography (CT) has revolutionised modern medical imaging. However, X-Ray CT imaging requires patients to be exposed to radiation, which can increase the risk of cancer. Therefore there exists an aim to reduce radiation doses for CT imaging without sacrificing image accuracy. This research combines phase retrieval with the ShallowU-Net CNN method to achieve the aim. This paper shows that a significant change in existing machine learning neural network algorithms could improve the X-ray phase retrieval in propagationbased phase-contrast imaging. This paper applies deep learning methods, through a variant of the existing U-Net architecture, named ShallowU-Net, to show that it is possible to perform two distance X-ray phase retrieval on composite materials by predicting a portion of the required data. ShallowU-Net is faster in training and in deployment. This method also performs data stretching and pre-processing, to reduce the numerical instability of the U-Net algorithm thereby improving the phase retrieval images.
Spatial resolution in standard phase-contrast X-ray imaging is limited by the finite number and size of detector pixels. As a result, this limits the size of features that can be seen directly in projection images or tomographic reconstructions. Dark-field imaging allows information regarding such features to be obtained, as the reconstructed image is a measure of the position-dependent small-angle X-ray scattering of incident rays from the unresolved microstructure. In this paper we utilize an intrinsic speckle-tracking-based X-ray imaging technique to obtain the effective dark-field signal from a wood sample. This effective dark-field signal is extracted using a Fokker-Planck type formalism, which models the deformations of illuminating reference-beam speckles due to both coherent and diffusive scatter from the sample. We here assume that (a) small-angle scattering fans at the exit surface of the sample are rotationally symmetric, and (b) the object has both attenuating and refractive properties. The associated inverse problem, of extracting the effective dark-field signal, is numerically stabilised using a “weighted determinants” approach. Effective dark-field projection images are presented, as well as the dark-field tomographic reconstructions obtained using Fokker-Planck implicit speckle-tracking.
With the GPU computing becoming main-stream, iterative tomographic reconstruction (IR) is becoming a com- putationally viable alternative to traditional single-shot analytical methods such as filtered back-projection. IR liberates one from the continuous X-ray source trajectories required for analytical reconstruction. We present a family of novel X-ray source trajectories for large-angle CBCT. These discrete (sparsely sampled) trajectories optimally fill the space of possible source locations by maximising the degree of mutually independent information. They satisfy a discrete equivalent of Tuy’s sufficiency condition and allow high cone-angle (high-flux) tomog- raphy. The highly isotropic nature of the trajectory has several advantages: (1) The average source distance is approximately constant throughout the reconstruction volume, thus avoiding the differential-magnification artefacts that plague high cone-angle helical computed tomography; (2) Reduced streaking artifacts due to e.g. X-ray beam-hardening; (3) Misalignment and component motion manifests as blur in the tomogram rather than double-edges, which is easier to automatically correct; (4) An approximately shift-invariant point-spread-function which enables filtering as a pre-conditioner to speed IR convergence. We describe these space-filling trajectories and demonstrate their above-mentioned properties compared with a traditional helical trajectories.
KEYWORDS: Radiography, Sensors, X-rays, Convolution, Signal to noise ratio, X-ray sources, 3D modeling, Data modeling, X-ray imaging, Signal attenuation
Micro scale computed tomography (CT) can resolve many features in cellular structures, bone formations, minerals properties and composite materials not seen at lower spatial-resolution. Those features enable us to build a more comprehensive model for the object of interest. CT resolution is limited by a fundamental trade off between source size and signal-to-noise ratio (SNR) for a given acquisition time. There is a limit on the X-ray flux that can be emitted from a certain source size, and fewer photons cause a lower SNR. A large source size creates penumbral blurring in the radiograph, limiting the effective spatial-resolution in the reconstruction.
High cone-angle CT improves SNR by increasing the X-ray solid angle that passes through the sample. In the high cone-angle regime current source deblurring methods break down due to incomplete modelling of the physical process. This paper presents high cone-angle source de-blurring models. We implement these models using a novel multi-slice Richardson-Lucy (M-RL) and 3D Conjugate Gradient deconvolution on experimental high cone-angle data to improve the spatial-resolution of the reconstructed volume. In M-RL, we slice the back projection volume into subsets which can be considered to have a relative uniform convolution kernel. We compare these results to those obtained from standard reconstruction techniques and current source deblurring methods (i.e. 2D Richardson-Lucy in the radiograph and the volume respectively).
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