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Prior-knowledge based iterative reconstruction for terahertz tomography.

Terahertz (THz) tomography is an up and coming technology that uses electromagnetic radiation with terahertz frequency for tomographic imaging. Like X-rays, THz waves provide information about the interior of an object through interaction with the object. THz waves interact with many materials in different ways. They are absorbed in polar materials such as water, penetrate most packing materials (plastic, paper, ceramics, …) and are completely reflected by metal. In contrast to X-rays, there are no known negative effects of THz waves, making their application attractive for biomedical purposes as well as industrial inspection, non-destructive testing, material science and agro-food applications. The Gaussian THz beam however, diverges much faster than an X-ray beam and reflection and refraction effects play a dominant role, preventing the use of conventional X-ray reconstruction techniques. In this project, we focus on the development of prior-knowledge based iterative reconstruction techniques for THz tomographic data that model the physics of the THz image formation in the image reconstruction process, as opposed to performing pre- or post-processing steps. Such algorithms are nearly unexplored for THz imaging and can greatly increase the applicability of the technique through a substantial improvement in image quality.
Date:1 Nov 2020 →  Today
Disciplines:Image processing, Applied and interdisciplinary physics, Signal processing, Biomedical image processing
Project type:Collaboration project