Tomography in the mobile setting has the potential to improve diagnostic outcomes by enabling 3D imaging at the patient’s bedside. Using component testing and system simulations, we demonstrate the potential for limited angle X-ray tomography on a mobile X-ray system. To enable mobile features such as low weight, size and power of system components, we have developed detector and patient anatomy tracking algorithms for accurately and automatically registering system geometry to patient anatomy during acquisition of individual projective-views along a tube-motion trajectory. We evaluate the effects of acquisition parameters and registration inaccuracy on image quality of reconstructed chest images using realistic X-ray simulation of an anthropomorphic numerical phantom of the thorax.
In general X-ray radiography, inconsistency of brightness and contrast in initial presentation is a common complaint from radiologists. Inconsistencies, which may be a result of variations in patient positioning, dose, protocol selection and implant could lead to additional workflow by technologists and radiologists to adjust the images. To tackle this challenge posed by conventional histogram-based display approach, an AI Based Brightness Contrast (AI BC) algorithm is proposed to improve the consistency in presentation by using a residual neural network trained to classify X-ray images based on N by M grid of brightness and contrast combinations. More than 30,000 unique images from sites in US, Ireland and Sweden covering 31 anatomy/view combinations were used for training. The model achieved an average test accuracy of 99.2% on a set of 2700 images. AI BC algorithm uses the model to classify and adjust images to achieve a reference look and then further adjust to achieve user preference. Quantitative evaluation using ROI based metrics on a set of twelve wrist images showed a 53% reduction in mean pixel intensity variation and a 39% reduction in bone-tissue contrast variation. A study with application specialists adjusting image presentation of 30 images covering 3 anatomies (foot, abdomen and knee) was performed. On average, the application specialists took ~20 minutes to adjust the conventional set, whereas they took ~10 minutes for the AI BC set. The proposed approach demonstrates the feasibility of using deep learning technique to reduce inconsistency in initial display presentation and improve user workflow.