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WP4

Advancing oncological PET imaging using machine learning

This WP will span over all three previous WPs with the objective to tackle limitations in the utilized imaging techniques. First, we will address well-known physical, biological and technical limitations in preclinical PET imaging that lower the quantification accuracy during kinetic analysis in imaging of small animals, by developing novel PET quantification methods using ML. Next, we will use established ML methods, as well as own developed techniques, to detect, visualize, and quantify changes in the tumor tissue automatically over time from PET image data. This will be necessary in order to quantify subtle changes in the treated tissue at early time points after therapy. Lastly, we will build ML models that, based on collected data, will be able to predict outcome and efficiency of a given treatment already at an early stage of the treatment. All these objectives will be essential for both brain, lung and breast cancer WPs.


Project Staff

 

Samuel Kuttner

Research Fellow (PhD Candidate)

Collaboration Partners

Researcher/Group
Roger Lecomte

Affiliation
Université de Sherbrooke, CA

 

 


 

 

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Principal Investigators



Stian Normann Anfinsen
Assoc. Prof., PhD

Associate Professor
Department of Physics and Technology
UiT The Arctic University of Norway


Robert Jenssen
Prof., PhD

Professor
Department of Physics and Technology
UiT The Arctic University of Norway