Dual modality neutron and x-ray tomography for enhanced image analysis of the bone-metal interface

The bone tissue formed at the contact interface with metallic implants, particularly its 3D microstructure, plays a pivotal role for the structural integrity of implant fixation. X-ray tomography is the classical imaging technique used for accessing microstructural information from bone tissue. However, neutron tomography has shown promise for visualising the immediate bone-metal implant interface, something which is highly challenging with x-rays due to large differences in attenuation between metal and biological tissue causing image artefacts. To highlight and explore the complementary nature of neutron and x-ray tomography, proximal rat tibiae with titanium-based implants were imaged with both modalities. The two techniques were compared in terms of visualisation of different material phases and by comparing the properties of the individual images, such as the contrast-to-noise ratio. After superimposing the images using a dedicated image registration algorithm, the complementarity was further investigated via analysis of the dual modality histogram, joining the neutron and x-ray data. From these joint histograms, peaks with well-defined grey value intervals corresponding to the different material phases observed in the specimens were identified and compared. The results highlight differences in how neutrons and x-rays interact with biological tissues and metallic implants, as well as the benefits of combining both modalities. Future refinement of the joint histogram analysis could improve the segmentation of structures and tissues, and yield novel information about specimen-specific properties such as moisture content.

Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.