AI Article Synopsis
- Optical imaging has been around for thousands of years, while radiographic imaging and tomography have developed significantly in the last 130 years, starting with the discovery of X-rays by Wilhelm Röntgen.
- Both optical and radiographic imaging share common building blocks such as physics, sources, detectors, methods, and data science, which enhance their capabilities.
- The evolution of these imaging techniques is pushing towards higher dimensions (like 4D) and is driven by technological advancements, aiming for better resolution, increased information yield, and integration of AI and automated data processing.
Article Abstract
Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt.61, RDS1 (2022)APOPAI0003-693510.1364/AO.455628. Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4 ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.
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