Fa-Hsuan Lin1,
Panu Vesanen2, Thomas Witzel, Risto Ilmoniemi, Juergen Hennig3
1A. A. Martinos Center, Charlestown,
MA, United States; 2Helsinki University of Technology, Helsinki,
Finland; 3University Hospital Freiburg, Freiburg, Germany
The
parallel imaging technique using localized gradients (PatLoc) system has the
degree of freedom to encode spatial information using multiple surface
gradient coils. Previous PatLoc reconstructions focused on acquisitions at
moderate accelerations. Compressed sensing (CS) is the emerging theory to
achieve imaging acceleration beyond the Nyquist limit if the image has a
sparse representation and the data can be acquired randomly and reconstructed
nonlinearly. Here we apply CS to PatLoc image reconstruction to achieve
further accelerated image reconstruction. Specifically, we compare the
reconstructions between PatLoc and traditional linear gradient systems at
acceleration rates in an under-determined system.