Yiyue Huang | Searcharxiv

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

Deep Learning in Classical X-ray Ghost Imaging for Dose Reduction

Ghost imaging (GI) is an unconventional technique that combines information from two correlated patterned light fields to compute an image of the object of interest. GI can be performed with visible light as well as penetrating radiation such as x-rays, electrons, etc. Penetrating radiation is usually ionizing and damages biological specimens; therefore, minimising the dose of this radiation in a medical or biological imaging context is important. GI has been proposed as a potential way to achieve this. With prior knowledge of the object of interest, such as sparsity in a specific basis (e.g., Fourier basis) or access to a large dataset for neural network training, it is possible to reconstruct an image of the object with a limited number of measurements. However, low sampling does not inherently equate to low dose. Here, we specifically explore the scenario where reduced sampling corresponds to low-dose conditions. In this simulation-based paper, we examine how deep learning (DL) techniques could reduce dose in classical x-ray GI. Since GI is based on illumination patterns, we start by exploring optimal sets of patterns that allow us to reconstruct the image with the fewest measurements, or lowest sampling rate, possible. We then propose a DL neural network that can directly reconstruct images from GI measurements even when the sampling rate is extremely low. We demonstrate that our deep learning-based GI (DLGI) approach has potential in image reconstruction, with results comparable to direct imaging (DI) at the same dose. However, given the same prior knowledge and detector quantum efficiency, it is very challenging for DLGI to outperform DI under low-dose conditions. We discuss how it may be achievable due to the higher sensitivity of bucket detectors over pixel detectors.

physics.optics↗