MR image reconstruction for non-Cartesian data and quantitative susceptibility maps using generalized sampling

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This projects aims to improve data acquisition and reconstruction for non-Cartesian MR-data, especially in the context of quantitative susceptibility imaging. An increasing amount of MR data is acquired non-Cartesianly which has various advantages compared to a classical Cartesian data acquisition, and the rather new field of quantitative susceptibility imaging is getting applied more and more in the area of neuroradiology, e.g. in imaging brain tumors, myelin and hemorrhage. Although promising first results have been reported, when viewed in depth, there are many difficulties. For example, the Cartesian sequences are susceptible to motion artifacts and require long acquisition times, and reconstruction algorithms are prone to artifacts. In this project, robust data acquisition and reconstructions which are free of artifacts shall be developed. This project includes aspects of data acquisition and reconstruction. Optimal sampling schemes for the generalized sampling reconstruction are to be simulated and implemented in acquisition sequences. Using wavelets and shearlets, reconstruction schemes based on generalized sampling will be designed. These schemes will also be used for the reconstruction of quantitative susceptibility maps. Furthermore, non-Fourier encoded data should be acquired with RF-pulses reproducing the previously used wavelets and shearlets. This non-Fourier encoded data shall, in turn, be reconstructed by the previously developed reconstruction algorithms.