Conical shell X-ray beam tomosynthesis and micro-computed tomography for microarchitectural characterisation.

Bone quality is commonly used to diagnose bone diseases such as osteoporosis, with many studies focusing on microarchitecture for fracture prediction. In this study a bovine distal femur was imaged using both micro-computed tomography (µCT) and tomosynthesis using focal construct geometry (FCG) for comparison of microarchitectural parameters. Six regions of interest (ROIs) were compared between the two imaging modalities, with both global and adaptive methods used to binarize the images. FCG images were downsampled to the same pixel size as the µCT images. Bone morphometrics were determined using BoneJ, for each imaging modality, binarization technique and ROI. Bone area/total area was found to have few significant differences between FCG and µCT (p < 0.05 for two of six ROIs). Fractal Dimension had only one significant difference (p < 0.05 for one of six ROIs) between µCT and downsampled FCG (where pixel size was equalized). Trabecular thickness and trabecular spacing were observed to follow trends as observed for the corresponding µCT images, although many absolute values were significantly different (p < 0.05 for between one and six ROIs depending on image types used). This study demonstrates the utility of tomosynthesis for measurement of microarchitectural morphometrics.

Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning.

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

Conical shell illumination incorporating a moving aperture for depth-resolved high-energy X-ray diffraction.

In many applications, the main limitation of X-ray absorption methods is that the signals measured are a function of the attenuation coefficient, which tells us almost nothing about the chemical or crystallographic nature of objects under inspection. To calculate fundamental crystallographic parameters requires the measurement of diffracted photons from a sample. Standard laboratory diffraction methods have been refined for well over a century and provide 'gold standard' structural models for well-prepared samples and single crystals but have little applicability for thick heterogeneous samples as demanded by many screening applications. We present a new high-energy X-ray diffraction probe, which in comparison with previous depth-resolving hollow beam techniques, requires a single beam, point detector and a simple swept aperture to resolve sample signatures at unknown locations within an inspection space. We perform Monte Carlo simulations to support experiments on both single- and multiple-material localisation and identification. The new probe is configured and tested using low-cost commercial components to provide a rapid and cost-effective solution for applications including explosives detection, process control and diagnostics.