arXiv subjects

Andrew Kingston

Publications and source records attributed to Andrew Kingston.

6 recordsLinked to original sources

arXiv2026

Measuring and Modelling Lag in Amorphous Silicon Flat-Panel X-ray Detectors

Detector lag, also referred to as afterglow, is a source of image degradation in flat-panel x-ray detectors, producing temporal artefacts that reduce image quality and quantitative accuracy. In this work, we present a robust and repeatable experimental framework for characterising long-term detector lag (from minutes to hours) under controlled step-up and step-down exposure transitions of particular relevance to tomography. The measured transition curves from an amorphous silicon detector with a CsI:Ti scintillator exhibit unexpected behaviour, with the detector response temporarily overshooting its final equilibrium intensity following step-up transitions, rather than exhibiting the gradual monotonic rise and decay typically expected. These curves were well fitted by multi-exponential functions, providing initial estimates for the depth of the charge traps in the scintillator. These parameters were incorporated into a proposed multi-trap rate equation model that reproduces the overall observed behaviour of both step-up and step-down transitions, including the overshoot that is not captured by previous models. Although discrepancies remain between the model and observation, this work establishes a reproducible methodology for detector lag characterisation and presents an improved physical model that offers greater insight into the mechanisms governing detector lag.

physics.ins-det↗

arXiv2024

Practical Global Backprojection-Convolution in Transmission Cone-beam Computed Tomography

Global backprojection-convolution (GBC) is a recently developed theory for exact reconstruction in transmission cone-beam computed tomography (CBCT). It is the first exact inversion theory that applies when the X-ray source points comprise a multidimensional `source locus' $X \subset \mathbb R^3$. Theoretically, GBC is computationally highly expedient due to its structure, but producing a practical computational implementation poses a significant challenge because the method is uniquely vulnerable to four sources of discretisation error: (1) accurate discretisation of a multidimensional locus requires more points than for a 1-dimensional locus, (2) the convolution kernel has infinite range and so the backprojected volume must be of infinite size, (3) the discrete convolution kernel cannot be computed in closed form, and (4) aliasing artefacts in the backprojection are enormously magnified by the convolution step. In this article, we propose an assortment of strategies to mitigate the discretisation errors, at the level of the symbolic algorithm. As a prototype, we deploy the concept on the case where $X$ is a cylinder. The resulting algorithm is evaluated through a series of reconstructions of the 3D Shepp-Logan phantom. As additional validation, we also briefly present a reconstruction from a real experimental dataset.

physics.med-ph↗

arXiv2024

Fractal Compressive Sensing

This paper introduces a sparse projection matrix composed of discrete (digital) periodic lines that create a pseudo-random (p.frac) sampling scheme. Our approach enables random Cartesian sampling whilst employing deterministic and one-dimensional (1D) trajectories derived from the discrete Radon transform (DRT). Unlike radial trajectories, DRT projections can be back-projected without interpolation. Thus, we also propose a novel reconstruction method based on the exact projections of the DRT called finite Fourier reconstruction (FFR). We term this combined p.frac and FFR strategy, finite compressive sensing (FCS), with image recovery demonstrated on experimental and simulated data; image quality comparisons are made with Cartesian random sampling in 1D and two-dimensional (2D), as well as radial under-sampling in a more constrained experiment. Our experiments indicate FCS enables 3-5dB gain in peak signal-to-noise ratio (PSNR) for 2-, 4- and 8-fold under-sampling compared to 1D Cartesian random sampling. This paper aims to: Review common sampling strategies for compressed sensing (CS)-magnetic resonance imaging (MRI) to inform the motivation of a projective and Cartesian sampling scheme. Compare the incoherence of these sampling strategies and the proposed p.frac. Compare reconstruction quality of the sampling schemes under various reconstruction strategies to determine the suitability of p.frac for CS-MRI. It is hypothesised that because p.frac is a highly incoherent sampling scheme, that reconstructions will be of high quality compared to 1D Cartesian phase-encode under-sampling.

eess.IV↗

arXiv2023

Synchrotron-based X-ray Fluorescence Ghost Imaging

X-ray Fluorescence Ghost Imaging (XRF-GI) was recently demonstrated for x-ray lab sources. It has the potential to reduce acquisition time and deposited dose by choosing their trade-off with spatial resolution, while alleviating the focusing constraints of the probing beam. Here, we demonstrate the realization of synchrotron-based XRF-GI: We present both an adapted experimental setup and its corresponding required computational technique to process the data. This extends the above-mentioned potential advantages of GI to synchrotron XRF imaging. In addition, it enables new strategies to improve resilience against drifts at all scales, and the study of previously inaccessible samples, such as liquids.

physics.optics↗

arXiv2010

Recovering Missing Slices of the Discrete Fourier Transform using Ghosts

The Discrete Fourier Transform (DFT) underpins the solution to many inverse problems commonly possessing missing or un-measured frequency information. This incomplete coverage of Fourier space always produces systematic artefacts called Ghosts. In this paper, a fast and exact method for de-convolving cyclic artefacts caused by missing slices of the DFT is presented. The slices discussed here originate from the exact partitioning of DFT space, under the projective Discrete Radon Transform, called the Discrete Fourier Slice Theorem. The method has a computational complexity of O(n log2 n) (where n = N^2) and is constructed from a new Finite Ghost theory. This theory is also shown to unify several aspects of work done on Ghosts over the past three decades. The paper concludes with a significant application to fast, exact, non-iterative image reconstruction from sets of discrete slices obtained for a limited range of projection angles.

math-ph↗

arXiv2010

Fast Mojette Transform for Discrete Tomography

A new algorithm for reconstructing a two dimensional object from a set of one dimensional projected views is presented that is both computationally exact and experimentally practical. The algorithm has a computational complexity of O(n log2 n) with n = N^2 for an NxN image, is robust in the presence of noise and produces no artefacts in the reconstruction process, as is the case with conventional tomographic methods. The reconstruction process is approximation free because the object is assumed to be discrete and utilizes fully discrete Radon transforms. Noise in the projection data can be suppressed further by introducing redundancy in the reconstruction. The number of projections required for exact reconstruction and the response to noise can be controlled without comprising the digital nature of the algorithm. The digital projections are those of the Mojette Transform, a form of discrete linogram. A simple analytical mapping is developed that compacts these projections exactly into symmetric periodic slices within the Discrete Fourier Transform. A new digital angle set is constructed that allows the periodic slices to completely fill all of the objects Discrete Fourier space. Techniques are proposed to acquire these digital projections experimentally to enable fast and robust two dimensional reconstructions.

physics.med-ph↗