- Programme: RealMaths
- ECTS Credits: 3
- Semester: 2
- Year: 2
- Campus: Claude Bernard University Lyon 1
- Content:
– Geometrical approaches for images and shapes.
– Variational methods for inverse problems in medical imaging.
– Sparsity and high dimension
– Neural networks
- Programme: RealMaths
- ECTS Credits: 3
- Semester: 2
- Year: 2
- Campus: Claude Bernard University Lyon 1
- Content:
– Neural networks
– Sparsity and high dimension
– Graphs and ecological networks
– Optimal transport for statistical learning
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Gdansk University of Technology
- Unit Coordinator: Tobias Knopp
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Hamburg University of Technology
- Language: English
- Aims:
After successful completion of the module, students are able to describe reconstruction methods for different tomographic imaging modalities such as computed tomography and magnetic resonance imaging.
They know the necessary basics from the fields of signal processing and inverse problems and are familiar with both analytical and iterative image reconstruction methods.
The students have a deepened knowledge of the imaging operators of computed tomography and magnetic resonance imaging.
- Content:
Overview about different imaging methods, signal processing, inverse problems, computed tomography, magnetic resonance imaging, compressed sensing, magnetic particle imaging.
- Pre-requisites:
Basic knowledge in linear algebra, numerics, and signal processing
- Reading list:
- Introduction to the Mathematics of Medical Imaging; C. L.Epstein; Siam, Philadelphia, 2008
- Medical Image Processing, Reconstruction and Restoration; J. Jan; Taylor and Francis, Boca Raton, 2006
- Principles of Magnetic Resonance Imaging; Z.-P. Liang and P. C. Lauterbur; IEEE Press, New York, 1999
