Acceleration of IDEAL Water-Fat Imaging Using Compressed Sensing

Samir D. Sharma¹, Harry H. Hu¹, Krishna S. Nayak¹

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.