Reconstruction of Undersampled MR Data Using Lp (0

Ganesh Adluru¹, Mark Rosen¹, Hee Kwon Song¹

¹Radiology, University of Pennsylvania, Philadelphia, PA, USA

Compressed sensing reconstruction using an L1 norm constraint offers high quality reconstructions from undersampled k-space data. The method is based on exploiting the implicit sparsity of the image. More recently it has been shown mathematically and using simulated numerical experiments that faithful reconstructions can be obtained from even fewer Fourier samples when an Lp constraint, where 0<p<1, is used. Here we test the feasibility of using the Lp spatial constrained reconstruction on in-vivo Cartesian MR images. Even though the cost function to be minimized is non-convex when p<1, results demonstrate that improved reconstructions can be consistently obtained.