Samuel Kuttner

Samuel Kuttner is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. He specializes in applying artificial intelligence to medical imaging challenges, with core expertise in Positron Emission Tomography (PET), deep learning algorithms, and quantitative image analysis. His work bridges machine learning with clinical applications in oncology and neurology.

Research Focus: Kuttner leads innovative projects at the intersection of AI and medical physics. His primary interests include:

• Developing non-invasive methods for arterial input function estimation in dynamic PET imaging
• Implementing deep learning for tumor segmentation and recurrence prediction in glioblastoma
• Optimizing quantitative PET/MRI through artifact correction and reconstruction algorithms
• Advancing physics-informed machine learning models for biomedical applications

Publication Trends: His recent articles (2020-2025) demonstrate strong focus on:

• AI-driven solutions for PET quantification challenges
• Clinical translation of radiomics and deep learning in neuro-oncology
• Methodological innovations for preclinical and clinical imaging workflows
• Interdisciplinary collaborations across machine learning, physics, and clinical medicine

Research Groups: He actively contributes to the Nuclear Medicine and Radiation Biology (NMRB) group and the Machine Learning Group at UiT, fostering cross-disciplinary research in medical technology innovation.