Since the development of the first X-ray computed tomography (CT) scanner by Godfrey Hounsfield in the early 1970s ( 2), CT scanners have become practically ubiquitous in hospitals around the world for the fast, non-invasive detection of a wide range of pathologies, from brain aneurysms to bone fractures and tumours. X-ray micro-computed tomography Introduction For both fields, I will give a brief introduction and illustrate how these technologies and methods can be combined specifically to shed light on the external and internal morphology of insects their outer form as well as their neuronal structure. Here, I present an overview of a (necessarily small) selection of the recent advances in those quickly expanding fields, with a focus on X-ray micro-computed tomography (♜T) and AI-supported approaches to analyse and interpret two- and three-dimensional (2D and 3D, respectively) image data gathered with ♜T. This computational revolution has driven the development and rise of, amongst many others, advanced imaging technologies as well as artificial intelligence (AI) with its subfields machine and deep learning. Some of the most obvious advances have taken place in the development of computer technology, with computing power and available space to store and access information increasing near exponentially over the last 50 years ( 1). Technological advances during the last decades have given researchers many new tools and methods that either opened up hitherto less approachable or even inaccessible areas of science or that enabled pushing the boundaries of already established fields. Furthermore, the combination of both approaches to investigate neural tissues and the resulting potential for the analysis of insect sensory systems, from receptor structures via neuronal pathways to the brain, are discussed. Here, I want to give a brief overview of both micro-computed tomography and deep learning and present their recent applications, especially within the field of insect science. Concurrently, advances in the field of artificial intelligence, especially in deep learning, have revolutionised computer vision and facilitated the automatic, fast and ever more accurate analysis of two- and three-dimensional image datasets. ♜T is hereby no longer limited to the analysis of specific biological tissues in a medical or preclinical context but can be combined with a variety of contrast agents to study form and function of all kinds of tissues and species, from mammals and reptiles to fish and microscopic invertebrates. In zoological research, this fast and non-destructive method for producing high-resolution, two- and three-dimensional images is increasingly being used for the functional analysis of the external and internal anatomy of animals. Institute of Biology, Karl-Franzens-University Graz, Graz, AustriaĪdvances in modern imaging and computer technologies have led to a steady rise in the use of micro-computed tomography (♜T) in many biological areas.Pickering focused on the development of particle beam profile monitors using laser wire systems, optical diffraction radiation, and optical transition radiation. As a researcher with CERN, DESY, and Cornell University, Dr.

He received a PhD in Accelerator Physics from Royal Holloway, University of London.

Thomas Pickering is OpticStudio Product Manager at Zemax. We will show you examples of how such a system can be set up and explain the ins and outs using multiple NSC groups.Ĭreating sequential systems with prisms, CAD parts, and other complex objects in OpticStudio - YouTube About the presenterĭr. In addition, the Non-Sequential Component surface can include multiple non-sequential objects, and you can insert multiple non-sequential groups. These mixed-mode systems make it easy to simulate complex beam paths, like multiple reflections inside of a prism. These systems, referred to as mixed-mode system or hybrid systems, use both sequential and non-sequential ray tracing.

This allows you to insert groups of non-sequential objects, like prisms, light pipes, or CAD parts, into what is otherwise a sequential system. The easiest way to simulate complex objects in a sequential ray trace is with a special sequential surface called the Non-Sequential Component surface.

Non sequential ray tracing