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Basic Introduction to Advanced MRI and Analysis Techniques for Neuro-Applications

Course 0000004627 in SS 2020

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Associate Professorship of Neuroradiology (Prof. Zimmer)
Lecturers Carl-Robert Ganter
Andreas Hock
Dimitrios Karampinos
Christine Preibisch
Franz Schilling
Afra Wohlschläger
Dates Wed, 16:00–17:30

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks Starting with a wrap up of the most important basics of MR signal generation and imaging, the lecture covers advanced magnetic resonance imaging (MRI), basic spectroscopy (MRS) as well as sophisticated functional MRI calibration and analysis methods. The primary aim is to give a wide overview on a variety of methods and provide the participants with a basic working knowledge on which structurally, physiologically and metabolically relevant information can be obtained by MRI and MRS, and how it can be refined by suitable analysis techniques. The course primarily aims at a conceptual understanding of the most important principles of the presented techniques and demonstrates relevant applications in clinical practice as well as research. It is intended to enable the participants to identify suitable applications and also recognize technological limitations. The range of covered techniques comprises quantitative relaxometry, diffusion weighted and tensor imaging, qualitative and quantitative oxygenation sensitive imaging (BOLD), qualitative and quantitative flow and perfusion imaging, qualitative and quantitative susceptibility sensitive techniques as well as metabolic imaging by means of MR spectroscopy. Likewise, the presentation of advanced analysis methods primarily aims at a basic understanding of more sophisticated techniques of structural and functional connectivity analysis, graph methods, functional parcellation, effective and dynamic functional connectivity. In addition, practical application examples will be given, relying on the capabilities of open source software packages for processing and analyzing brain MRI.
Links E-Learning course (e. g. Moodle)
TUMonline entry
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