• MRI simulation and data acquisition schemes

• Iterative reconstruction techniques

• Sparse image representations (Wavelets, Total Variation)

• GPU acceleration / parallelization of performance-critical tasks

Projects

Topic 1: Compressed Sensing MRI
Compressed Sensing (CS) algorithms are a powerful family of iterative reconstruction techniques that exploit sparsity in a certain transform domain.

Tasks:

• Literature research on CS.

• Implementation (Matlab/C++) of one CS algorithm within the provided Framework (e.g. ISTA-type).

• Comparison against other, already implemented algorithms.

Topic 2: Radial Sampling
Radial trajectories are a recent trend in MRI but require a non-uniform FFT for reconstruction. Therefore, iterative reconstruction algorithms sometimes simulate the trajectory by interpolation onto a Cartesian grid.
Tasks:

• Study noise tolerance of radial trajectories vs. cartesian trajectories.

• Examine whether pseudo-radial sampling has a similar artifact behaviour as true radial sampling.

• Implementation: preferrably C++

Topic 3: Prior image regularization
Especially in dynamic imaging, the difference to a prior image is sometimes used for regularization. The same methods can be used to evaluate quality if a reference image is available. An interesting use case are contrast enhanced examinations, where the perfusion can be assessed by injecting contrast agent.

Tasks:

• C++ Implementation of a prior image constraint (L1/L2/local).

• Evaluation of the new regularization term.