The Computational Nuclear Medicine group focuses on the application of artificial intelligence and other computational approaches such as network modeling 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
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
Clemens Spielvogel, PhD
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
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:
Dipl.-Ing. David Haberl
Research interests:
- Medical Imaging and Computational Image Analysis
- Deep Learning, Computer Vision
Links:
Jing Ning, MD
Research interests:
-
Nuclear medicine and molecular imaging in oncology
-
Multi-omics data and analysis
Links:
Iustin Tibu
- Study program: Clinical medicine (Dr. med)
- Research topic: Stress biomarkers in lung cancer
Markus Köfler, BSc
- Study program: Master Data Science
- Research topics: Machine learning and segmentation in cardiac amyloidosis characterization
Michael Beyerlein
- Study program: Clinical medicine (Dr. med)
- Research topic: Stress biomarkers in breast cancer
- Christophoros Eseroglou, MSc. (Master student)
Selected Publications
Spielvogel, C. P. 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
Ning, J. 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
Xue, S. et al. A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain. Eur. J. Nucl. Med. Mol. Imaging 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
Spielvogel, C. P. et al. Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer. Eur. J. Nucl. Med. Mol. Imaging 2023
Papp, L. et al. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. Eur. J. Nucl. Med. Mol. Imaging 2021
Geist, B. K. et al. A methodological investigation of healthy tissue, hepatocellular carcinoma, and other lesions with dynamic 68Ga-FAPI-04 PET/CT imaging. EJNMMI Phys. 2021
Geist, B. K. et al. Comparison of different kinetic models for dynamic 18F-FDG PET/CT imaging of hepatocellular carcinoma with various, also dual-blood input function. Phys. Med. Biol. 2020