Noise analysis for 3-point chemical shift-based water-fat separation with spectral modeling of fat.

Purpose: To model the theoretical signal-to-noise ratio (SNR) behavior of 3-point chemical shift-based water-fat separation, using spectral modeling of fat, with experimental validation for spin-echo and gradient-echo imaging. The echo combination that achieves the best SNR performance for a given spectral model of fat was also investigated.

Materials And Methods: Cramér-Rao bound analysis was used to calculate the best possible SNR performance for a given echo combination. Experimental validation in a fat-water phantom was performed and compared with theory. In vivo scans were performed to compare fat separation with and with out spectral modeling of fat.

Results: Theoretical SNR calculations for methods that include spectral modeling of fat agree closely with experimental SNR measurements. Spectral modeling of fat more accurately separates fat and water signals, with only a slight decrease in the SNR performance of the water-only image, although with a relatively large decrease in the fat SNR performance.

Conclusion: The optimal echo combination that provides the best SNR performance for water using spectral modeling of fat is very similar to previous optimizations that modeled fat as a single peak. Therefore, the optimal echo spacing commonly used for single fat peak models is adequate for most applications that use spectral modeling of fat.