KI-INSPIRE: Verbund - KI: Künstliche Intelligenz für den innovativen nachhaltigen Strahlenschutz von Patienten in interventionellen radiologischen Einsatzgebieten

Artificial intelligence (AI) opens many possibilities for radiation protection in medical imaging. To date, medical imaging examinations represent almost 100% of the civilian radiation exposure. On the other hand, an enormous dose saving potential could be achieved thanks to the new disruptive technologies of AI.
The goal of this collaborative project is to develop, implement and test AI-based methods to significantly reduce radiation dose in medical imaging with ionizing radiation without jeopardizing image quality.
In order to enable a holistic and systematic approach, the project addresses interventional imaging, where both diagnostic and therapeutic goals are based on computed tomography (CT), angiography and nuclear medicine imaging techniques such as PET.
A specific focus of KI-INSPIRE lies on the development and establishment of intelligent algorithms for (I) dose reduction, (II) image quality improvement and (III) motion artifact reduction, as well as for (IV) interventional tissue characterization in medical radiation applications - applications that are all related to radiation protection. The focus here is on increasing safety for patients and medical personnel, so that a valuable contribution can be made to the positive perception of AI in the general population.

Focus of the research project is the development of a reconstruction strategy to process two raw data sets of the same object from different modalities in one reconstruction formulation and therefore facilitate mutual assistance of both imaging techniques. This should enhance the results with respect to contrast, object boundaries and noise statistics.

Grants

  • Federal Ministry for the Environment, Nature, Conservation and Nuclear Safety under grant number BMU 67KI2036C

Publications

2023[ to top ]
  • Pommranz, C., Elmoujarkach, E., Cabello, J., Lan, W., Rafecas, M., Mannheim, J., Santangelo, A., Fougère, C. L., Pichler, B. and Schmidt, F.: Development and Initial Validation of Two Simulation Workflows Using GATE for a Total-Body PET/CT Scanner, 1–1, 2023, DOI: 10.1109/NSSMICRTSD49126.2023.10338368.
  • Pommranz, C., Elmoujarkach, E., Cabello, J., Lan, W., Rafecas, M., Mannheim, J., P.Linder, Santangelo, A., Fougère, C., Pichler, B. and Schmidt, F.: Development and Validation of a Monte Carlo Simulation Workflow for a Total-Body PET Scanner, 690–691, 2023, DOI: 10.1007/s00259-023-06333-x.
  • Pommranz, C., Elmoujarkach, E., Cabello, J., Rafecas, M., Mannheim, J., Santangelo, A., la Fougère, C., Pichler, B. and Schmidt, F.: Simulation Studies and Experimental Model Validation of the Biograph Vision Quadra, Nuklearmedizin - NuclearMedicine, 62(02), V8-, 2023, DOI: 10.1055/s-0043-1766212.
  • Elmoujarkach, E., Seeger, S., Schmidt, C., Mannheim, J. G., Schmidt, F. P. and Rafecas, M.: First 3D printed radioactive 89Zr phantoms for Positron Emission Tomography, Transactions on Additive Manufacturing Meets Medicine, Vol. 5 No. S1 (2023): Trans. AMMM Supplement, 2023, DOI: 10.18416/AMMM.2023.2309833.
2022[ to top ]
  • Byl, A., Knaup, M., Rafecas, M., Hoeschen, C. and Kachelrieß, M.: Detruncation of clinical CT scans using a discrete algebraic reconstruction technique prior, 2022, DOI: 10.1117/12.2646885.
  • Elmoujarkach, E., Seeger, S., Möller, N., Schmidt, C. and Rafecas, M.: Development and Characterization of 3D Printed Radioactive Phantoms for High Resolution PET, 1–2, 2022, DOI: 10.1109/NSS/MIC44845.2022.10399242.
  • Seeger, S., Elmoujarkach, E., Möller, N., Schmidt, C. and Rafecas, M.: 3D printed radioactive phantoms for Positron Emission Tomography, ID 640, 2022, DOI: 10.18416/AMMM.2022.2209640.