Ultrafast Radiographic Imaging and Tracking: An overview of instruments, methods, data, and applications

TL;DR

Ultrafast radiographic imaging and tracking (U-RadIT) employs advanced sources, detectors, and data algorithms to study rapid processes across sciences, with recent progress in hardware, data interpretation, and machine learning enhancing its capabilities.

Contribution

This paper provides a comprehensive overview of recent advancements in U-RadIT, focusing on hardware, data processing, and applications, highlighting the integration of machine learning for improved analysis.

Findings

Progress in detector technology and U-RadIT modalities.

Use of machine learning and compressed sensing for data interpretation.

Potential for enhanced imaging and tracking in various scientific fields.

Abstract

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes, fundamental to nuclear fusion energy, advanced manufacturing, green transportation and others, often involve one mole or more atoms, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: a.) Detectors; b.) U-RadIT modalities; c.) Data and algorithms; and d.) Applications. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification, and U-RadIT optimization.