The research of our Computational Nuclear Medicine groups focuses on the application of artificial intelligence, network modeling and other computational approaches to improve clinical procedures and enhance our understanding of health and disease. The research is centered around nuclear medicine, integrating diagnostic, therapeutic, and basic research aspects.
Dr. Barbara Geist
Group Leader - Geist Lab
Research interests:
- Quantification of dynamic data, kinetic modeling
- Organ connectomes, network analyses
- Stress determination and effect in PET imaging
- Quantification of metabolic changes in PET imaging
Contact:
- Mail: barbara.geist@meduniwien.ac.at
- Phone: +43 1 40400 72350
Joel Fischer, MSc
Doctoral Researcher - Geist Lab
Research interests:
- KI und Machine Learning
- Integration of biochemistry and data analysis
Links:
Andreea Pavel, BSc
Master Student - Geist Lab
- Study program: MSc Biomedical Engineering, Medical Physics and Imaging track (Technical University of Vienna)
- Research topic: Multicohort Whole-Body Metabolic Partial Correlation Networks for Clinical Outcome Assessment
Adna Smajlovic, BSc
Master Student - Geist Lab
- Study program: Biomedical Engineering mit Schwerpunkt Medical Physics and Imaging (MSc, Vienna University of Technology)
- Research Topic: Multicohort wholebody PSMA connectomes to evaluate the role of the thyroid
Past Members
- Dato Tsomaia (Bachelor student)
Clemens Spielvogel, PhD
Group Leader - Spielvogel Lab
Research interests:
- Clinical and biomedical applications of artificial intelligence and computational imaging
- Opportunistic risk markers
- Computational cardiovascular imaging
- Cardiac amyloidosis
Links:
Contact:
- Mail: clemens.spielvogel@meduniwien.ac.at
- Phone: +43 1 40400 72350
Peyman Sharifian, MSc
Doctoral Researcher - Spielvogel Lab
Research interests:
- Application of artificial intelligence for cardiovascular imaging
Links:
Lukas Zulus, BSc
Master Student - Spielvogel Lab
- Study program: Med Tech (MSc, FH Wiener Neustadt)
- Research Topic: Organ and cardiometabolic fingerprints
Past Members
- Carolina Rodrigues (Bachelor student)
- David Haberl, PhD (PhD students)
- Oleg Gergets, BSc (Bachelor student)
- Markus Köfler, BSc (Master student)
- Christophoros Eseroglou, MSc (Master student)
- Lars Böhmer, BSc (Master student)
- Diana Maaßen, BSc (Master student)
Song Xue, PhD
Research interests:
- Ultra-low dose PET imaging with AI
- Individualized dosimetry for radiopharmaceutical therapy
- Biomarkers identification for prognosis and therapy response
- AI-driven radiopharmaceutical discovery and development
Links:
Contact:
Past Members
- Zewen Jiang (PhD student)
- Jing Ning, MD, PhD (PostDoc)
- Michael Beyerlein, MD (Diploma student)
- Iustin Tibu, MD (Diploma student)
Selected Publications
Spielvogel, C.P. et al. Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study. PLOS Digital Health 2026
Spielvogel, C.P. et al. Impact of disease-modifying therapy on [99mTc]Tc-DPD SPECT/CT markers in transthyretin cardiac amyloidosis enabled by artificial intelligence. Eur. J. Nucl. Med. Mol. Imaging 2025
Geist, B.K. et al. The metabolic organ connectome: A novel approach to measure allostatic load during health-to-disease transition. Med 2025
Spielvogel, C.P. et al. Artificial intelligence-enabled opportunistic identification of immune checkpoint inhibitor-related adverse events using [18F]FDG PET/CT. Eur. J. Nucl. Med. Mol. Imaging 2025
Hong, Z., Spielvogel, C.P. et al. Enhanced Diagnostic and Prognostic Assessment of Cardiac Amyloidosis Using Combined 11C-PiB PET/CT and 99mTc-DPD Scintigraphy. Eur. J. Nucl. Med. Mol. Imaging 2025
Haberl, D. et al. Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments. Eur. J. Nucl. Med. Mol. Imaging 2025
Spielvogel, C.P. et al. Enhancing Blood–Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database. Journal of Chemical Information and Modeling 2025
Spielvogel, C. P., Haberl, D. et al. 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. Lancet Digit Health 2024
Spielvogel, C.P., Ning J. et al. Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [68Ga]Ga-PSMA-11 PET/MRI. Insights into Imaging 2024
Ning, J., Spielvogel, C.P. et al. A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study. Theranostics 2024
Haberl, D. et al. Multicenter PET image harmonization using generative adversarial networks. Eur. J. Nucl. Med. Mol. Imaging 2024
Yu, J. et al. Systemic Metabolic and Volumetric Assessment via Whole-Body [F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma. Cancers 2024
Geist, B. K. et al. In vivo assessment of safety, biodistribution, and radiation dosimetry of the [18F]Me4FDG PET-radiotracer in adults. EJNMMI Res. 2024