Enrolement key: advancedNUK

  • General

    As the amount of data in medicine has increased steadily over the last decades, the analysis methods have evolved as well. This lecture will recap the principles of nuclear medicine imaging and introduce you to the basics in state-of-the-art data analysis techniques.

    Our target audience includes interested students from the fields of medicine, physics, and data sciences.

    If there are any questions, please do not hesitate to contact us

    • Overview

      Nuclear medicine originated half a century ago and is still the subject of current research and common in clinical routine. Technical developments allowed more and more data to be acquired and stored during measurements and rendered it necessary to improve analysis techniques.

      We will start with an overview of nuclear medicine devices, especially imaging modalities and the corresponding vocabulary. Based on this knowledge we will cover different data processing approaches e.g. using Monte Carlo simulations, pharmacokinetic modelling, and machine learning. In this context examples of applications in clinical routine and ongoing research will be discussed.

      Our goal is to provide you with information necessary to achieve high medical standards and good research performances in the interdisciplinary field of clinical environment.

      • Course Schedule

        28.04.2023: Nuclear medicine basics
        05.05.2023: Image processing and analysis basics
        12.05.2023: Pharmacokinetic modelling I
        19.05.2023: Pharmacokinetic modelling II
        26.05.2023: Radiomics in nuclear medicine
        02.06.2023: Monte Carlo simulation for imaging
        09.06.2023: PET based brain connectivity analysis
        16.06.2023: Machine learning concepts I
        21.06.2023: Machine learning concepts II
        28.06.2023: Machine learning applications in nuclear medicine
        07.07.2023: Radionuclide therapy and theranostics
        14.07.2023: Dosimetry concepts in radionuclide therapy
        21.07.2023: Repetition
        28.07.2023: Exam