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Home Projects 1st call (2008) 10. Improved magnetic resonance image reconstruction via wavelet regularization

10. Improved magnetic resonance image reconstruction via wavelet regularization

Dr. M. Unser (EPFL), Dr. K.P. Prüssmann (ETHZ) - PhD student: Matthieu Guerquin-Kern

Project finished in July 2012.

Classical MRI uses cartesian sampling in k-space which facilitates the reconstruction process (simple inverse Fourier transform). Currently, the research focus is on more general acquisition schemes (non Cartesian, with missing data, ...) that offer more flexibility, but require more complicated reconstruction procedures. The state-of-the-art algorithms are linear, but there is evidence that the quality of reconstruction can be improved by using non-linear iterative techniques.

To have a better conditioned reconstruction problem, we propose to favor solutions that have a sparse representation in the wavelet domain (which is the case for natural looking images). Our mathematical efforts will focus on the design of a fastly converging algorithm that exploits the specificity of the problem setting at hand. As a first step, we will design an algorithm adapted to the single-coil case that can handle general non-cartesian k-space sampling. Next, we will extend it to the multiple-coil problem. We will apply the reconstruction process to both simulated and real data in order to validate it. We expect superior results in quality and speed with respect to the current state-of-the-art techniques.

Contact: Matthieu Guerquin-Kern