CC BY 4.0Hellwege, LauraLauraHellwegeGensana Claus, CarlaCarlaGensana ClausSchaar, MoritzMoritzSchaarBuzug, ThorstenThorstenBuzugStille, MaikMaikStille2025-11-102025-11-102025-09-01https://publica.fraunhofer.de/handle/publica/498870https://doi.org/10.24406/publica-612210.1515/cdbme-2025-024210.24406/publica-6122Multi-energy reconstructions have become an important research field in computed tomography in recent years. Since modern reconstruction and postprocessing techniques often employ deep learning strategies, there is a high need for large, diverse and adaptable multi-energy datasets. Therefore, this work proposes a straightforward pipeline for the generation of multi-energy cone-beam CT projection data based on the established XCAT software phantom with arbitrary desired X-ray spectra. We evaluate the effort and time required for dataset generation and utilize the generated data for model-based iterative reconstruction exemplarily. This approach provides an understanding of the current pipeline’s bottlenecks while demonstrating its suitability in producing high-quality projection datasets and reconstructions. Thus, we contribute to open knowledge on generation of large multi-energetic CT datasets for deep learning purposes.enMulti-energy CTsimulationdeep learningdata generationjournal article