Muhammad Usman1, Philip G. Batchelor1
1King's College London, London, United
Kingdom
The
L1 minimization technique has been empirically demonstrated to exactly
recover an S-sparse signal with about 3S-5S measurements. In order to get
exact reconstruction with smaller number of measurements, recently, for
static images, Trzasko has proposed homotopic L0 minimization technique.
Instead of minimizing the L0 norm which achieves best possible theoretical
bound (approximately 2S measurements) but is a NP hard problem or L1 norm
which is a convex optimization problem but requires more measurements, the
homotopic technique minimizes iteratively the continuous approximations of
the L0 norm. In this work, we have extended the use of homotopic L0 method to
dynamic MR imaging. For dynamic 2D CINE data, using five different non-convex
functional approximations to L0 norm, we have compared the performance of
homotopic L0 minimization technique with the standard L1 method.