Phase-constrained Magnetic Resonance Imaging as a Nonlinear Inverse Problem with a Rank PenaltyPP

Phase constraints in parallel Magnetic Resonance Imaging (MRI) allow improved reconstruction by exploiting that the image is real-valued. However, in practice, high-frequency phase variations may occur and may lead to artifacts. Here, we present a formulation of phase-constrained parallel MRI as a nonlinear inverse problem and show how it is related to a linear inverse problem subject to a rank penalty. This method is robust and efficient, and allows artifact-free reconstruction in these cases.

This is poster number 14 in Poster Session

H. Christian M. Holme (University Medical Center Göttingen)
Sebastian Rosenzweig (University Medical Center Göttingen)
Martin Uecker (University Medical Center Göttingen)
image reconstruction, inverse problems, magnetic resonance imaging, nonlinear optimization