Fat-water separation in dynamic objects using an UNFOLD-like temporal processing

To separate fat and water signals in dynamic imaging. Because important features may be embedded in fat, and because fat may take part in disease processes, separating fat and water signals may be of great importance in a number of clinical applications. This work aims to achieve such separation at nearly no loss in temporal resolution compared to usual, nonseparated acquisitions. In contrast, the well-known 3-point Dixon method may cause as much as a 3-fold reduction in temporal resolution.

The proposed approach involves modulating the echo time TE from frame to frame, to force fat signals to behave in a conspicuous manner through time, so they can be readily identified and separated from water signals. The strategy is inspired from the "unaliasing by Fourier encoding the overlaps in the temporal direction" (UNFOLD) method, although UNFOLD involves changes in the sampling function rather than TE, and aims at suppressing aliased material rather than fat.

The method was implemented at 1.5 T and 3 T, on cardiac cine and multiframe steady-state free precession sequences. In addition to phantom results, in vivo results from volunteers are presented.

Good separation of fat and water signals was achieved in all cases.