Project
A-priori-knowledge enhanced CT reconstruction for fast scanning strategies
This PhD research aims at enabling fast in-line computed tomography scanning (CT) by reducing the number of projections required while maintaining a good signal-to-noise ratio (SNR). A priori data will be exploited to enhance the quality and reduce CT artefacts in CT reconstructions, especially in case of multi-material work pieces. Starting from representative high-quality scans of different object classes, the number of projections will be incrementally reduced, and parallel approaches toward sparse-projection reconstruction will be applied, one of which makes use of the priori information available of the object. Earlier work has shown the potential of sparse-projection and/or model-based reconstruction. It is expected to find an independent in-line reconstruction method that exploits a prior information to deliver high quality data with good SNR and limited CT artefacts out of few projections and its related demonstrator software for a novel CT system. The methods will be evaluated on academic parts as well as on an industrially relevant case study.