Detail

Keywords

Artificial Intelligence; Biomedical Research; Molecular Imaging; Monte Carlo Method; Pattern Recognition, Automated; Positron-Emission Tomography

Research group(s)

• Christian Doppler Laboratory for Applied Metabolomics
  Head: Lukas Kenner
  Research Area: We investigate ways to better characterize tumors using non-invasive imaging techniques. This is important because tumours are constantly changing through mutations. In this way, an individual therapy should be possible and its success should be continuously monitored.
  Members:
  Marko Grahovac
  Kilian Kluge
  Lukas Kenner
  Jing Ning
  David Haberl
• Computational Nuclear Medicine
  Head:
  Members:
  Josef Yu
  Jing Ning
  Barbara Katharina Geist
  Clemens P. Spielvogel
  David Haberl

Research interests

Exploiting molecular imaging data to capture potential marker candidates for improved diagnosis and treatment stratification.

Techniques, methods & infrastructure

• Deep Learning
• Computer Vision
• Medical Image Analysis
• Monte Carlo Simulation (Geant4, GATE)

Selected publications

1. Haberl, D. et al. (2025) ‘Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-025-07091-8.
2. Spielvogel*, Haberl* et al. (2024) ‘Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study’, The Lancet Digital Health, 6(4), pp. e251–e260. Available at: https://doi.org/10.1016/s2589-7500(23)00265-0.
3. Haberl, D. et al. (2024) ‘Multicenter PET image harmonization using generative adversarial networks’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-024-06708-8.