Magnetic Resonance Imaging (MRI) is a powerful tomographic imaging technique based on sampling of Fourier data in a series of measurements. To reduce the number of measurements, parallel imaging utilizes data from multiple receive coils which each produces a signal modulated by a different spatial sensitivity profile. We will discuss robust algorithms for parallel imaging that avoid explicit calibration of the sensitivities by formulating reconstruction as a non-linear inverse problem.

This presentation is part of Minisymposium “MS25 - Bilinear and quadratric problems in imaging”
organized by: Felix Krahmer (Technical University of Munich, Department of Mathematics) , Kristian Bredies (Universität Graz) .

Authors:

Martin Uecker (University Medical Center Göttingen)

Sebastian Rosenzweig (University Medical Center Göttingen)

H. Christian M. Holme (University Medical Center Göttingen)

Keywords:

blind deconvolution, computational imaging, image reconstruction, inverse problems, magnetic resonance imaging, nonlinear optimization