The ability of MRI to discriminate between water and fat is important in tissue characterization. It has been shown that fibrofatty infiltration of the myocardium is associated with sudden death [1
] and, therefore, non-invasive detection could have high prognostic value. Conventional approaches to fat and water discrimination based on fat suppression are commonly used to characterize masses but have reduced ability to characterize myocardial fatty infiltration due to the poor contrast of microscopic fat and partial volume effects. Multi-echo Dixon methods [2
] for fat and water separation provide a sensitive means of detecting small concentrations of fat with improved contrast. In this study, these methods have been applied to the detection of fibro-fatty infiltration observed in chronic myocardial infarction (MI) as well cases of suspected arrhythmogenic right ventricular cardiomyopathy (ARVC). Fat and water separation has been implemented both pre-contrast as well as applied to late enhancement using a multi-echo PSIR-GRE sequence.
Approaches to characterizing tissue fat content include [9
] (1) use of chemical shift saturation to suppress fat, (2) use of T1-weighted imaging to detect T1 shortening of fat which may be observed as bright signal intensity, and (3) use of multi-echo Dixon methods to reconstruct water and fat separated images [2
]. In fat suppression imaging, regions with fat will appear dark, and the absence of signal relative to non-fat suppressed images will indicate presence of fat. A disadvantage of this approach is that it requires acquiring both fat suppressed and non-suppressed images. Furthermore, the performance of fat suppression depends on the field homogeneity. Importantly, for application in characterizing fatty infiltration in which voxels may have a low concentration of fat, the decrease in signal intensity (or negative contrast) is often difficult to discern from other signal intensity fluctuations. For this reason, the diagnosis of intramyocardial fat using conventional fat suppression can be subjective.
T1-weighted imaging may be used to detect fat [9
] and T1-weighted inversion recovery (IR) has been used to detect fatty infiltration in chronic MI [11
] prior to contrast administration. In this approach, an IR-cine SSFP sequence was used to acquire images at multiple inversion times. Regions with significant fat content were observed to have a shorter null time. Since this technique is performed prior to contrast, it may be difficult to precisely correlate with late enhancement images acquired at different cardiac phase and separate breath-holds. Further, additional pre-contrast breath-hold images must be acquired prior to having the late enhancement images which in some cases may be the first recognition of MI.
Multi-echo methods for fat and water separation are based on the difference in resonance frequencies between water and fat. Dixon’s original method for water and fat separation [2
] acquires two images with different echo times chosen such that the water and fat are in-phase and opposed-phase, respectively, and may be combined to obtain separate water and fat images. This simple method assumes that the water is exactly on resonance which limits the performance of water and fat separation in the presence of B0-field inhomogeneity. A three-point method [3
] was introduced to allow estimation of the background field at each voxel. In this method, the echo times are also set such that the water and fat are in-phase and opposed-phase in order to simplify estimation of the background fieldmap. However, phase ambiguities arising from large field variation can still lead to incorrect assignment of water and fat pixels. Phase unwrapping [4
] has been proposed to deal with this issue, however, phase unwrapping methods are often not robust in cardiac imaging applications due to high field inhomogeneity [12
] as well as relatively low SNR.
Recent methods [5
] for multi-echo Dixon water and fat separation jointly estimate the fieldmap, water, and fat images. These methods apply spatial constraints in the process of fieldmap estimation which is generally more robust than phase unwrapping. These methods may also be used with arbitrary echo times which allows for more flexible sequence and protocol design. One method to solve this nonlinear estimation problem, termed iterative decomposition with echo asymmetry and least-squares (IDEAL) [5
], consists of repeated linearizations of the original nonlinear problem, alternatively estimating the water and fat signals and the field map. A second method is the variable projection (VARPRO) method [8
] which formulates the solution as a separable non-linear least squares problem. VARPRO attains the globally optimum maximum-likelihood (ML) solution and the implementation is very robust for the proposed cardiac application with large background field variation and low signal-to-noise ratio. VARPRO was found to be more robust [8
] in terms of correct classification of water and fat than our implementation of IDEAL and was, therefore, used instead throughout the study.
The proposed use of multi-echo Dixon water-fat separation to chacterize intramyocardial fat has a number of potential benefits. The multi-echo Dixon method combined with fieldmap estimation [5
] produces excellent discrimination between water and fat. The fat separated image provides positive contrast (containing only signal from lipids) which improves the diagnostic confidence. The proposed water and fat separation method may be combined with late enhancement to provide positive correlation between fibrosis and fat, which both appear bright post-contrast. The water and fat images are spatially registered since they are reconstructed from the same multi-echo dataset. Furthermore, chemical shift artifacts may be eliminated in reconstruction.
It was hypothesized that the multi-echo Dixon method of fat-water imaging could be used to detect intramyocardial fat. Multi-echo GRE fat-water imaging was performed on 28 patients with either known or suspected coronary artery disease, or with suspicion of intramyocardial fat.