Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm

Abstract

Water/fat separation is a classical problem for in vivo proton MRI. Although many methods have been proposed to address this problem, robust water/fat separation remains a challenge, especially in the presence of large amplitude of static field inhomogeneities. This problem is challenging because of the nonuniqueness of the solution for an isolated voxel. This paper tackles the problem using a statistically motivated formulation that jointly estimates the complete field map and the entire water/fat images. This formulation results in a difficult optimization problem that is solved effectively using a novel graph cut algorithm, based on an iterative process where all voxels are updated simultaneously. The proposed method has good theoretical properties, as well as an efficient implementation. Simulations and in vivo results are shown to highlight the properties of the proposed method and compare it to previous approaches. Twenty-five cardiac datasets acquired on a short, wide-bore scanner with different slice orientations were used to test the proposed method, which produced robust water/fat separation for these challenging datasets. This paper also shows example applications of the proposed method, such as the characterization of intramyocardial fat.

Water-fat separation incorporating spatial smoothing is robust to noise

“…In the original paper, two spin echoes were used, and the fat signal was modeled as a single peak. Today, spoiled gradient echo sequences are often used, and many methods have been developed that can make use of arbitrarily many echoes (multi-echo methods) [2][3][4][5][6][7][8][9][10][11][12][13]. Additionally, a multi-peak fat spectra [3][4][5][6][7][8][9][10][11][14][15][16][17] may be employed for improved signal separation.…”

“…One problem with region growing approaches is that when swaps do occur, they typically propagate well beyond the voxels where they originate [5]. A potentially more robust alternative to region growing are graph cuts [6][7][8][9][10][11]15,16], which can optimize entire slices or whole volumes at once.…”

Evaluation of adipose tissue volume quantification with IDEAL fat–water separation

“…Yu et al (23), reported a fat-water separation failure rate of 1.8% for IDEAL, which is consistent with our experience. We note that recently there has been considerable research regarding more reliable methods of B 0 field mapping for fat-water separation (24,25) and implementation of some of these newer methods will reduce the failure rate even further. The lower value of visceral adipose tissue volume given by IDEAL relative to 3.0T SPGR imaging (9.9% versus 14.8%) reflects an improved ability to differentiate between true visceral adipose tissue and bright structures like blood vessels that could be mistaken for adipose tissue in the non-fat suppressed T1-weighted images as shown.…”

Correction of Fat-Water Swaps in Dixon MRI

“…Robust estimation of the inherent fat-water separation is an active field of research, and an overview is provided in [7]. There are several extensions [8] and alternatives to the original IDEAL method, including graph-cut formulations [7] and other discrete optimization approaches [9,10].…”

“…There are several extensions [8] and alternatives to the original IDEAL method, including graph-cut formulations [7] and other discrete optimization approaches [9,10]. All require access to the original echo sequences or a B0 field map [11] as part of their optimization procedure.…”