Samir D. Sharma1, Harry H. Hu1,
Krishna S. Nayak1
1Department of Electrical Engineering,
University of Southern California, Los Angeles, CA, United States
IDEAL
is a robust iterative technique for estimating water and fat signals on a
voxel-basis, based on multi-echo data.
In each iteration, two least-squares problems are solved. In this work, we reformulate each of the
least-squares problems and solve them via Compressed Sensing (CS). We exploit the compressibility of both the
water and fat images as well as smoothness of the field map to regularize our
underdetermined systems of equations.
The result is an up to 3x acceleration from the conventional IDEAL method.