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


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.


CONCLUSIONS
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.
Chebrolu
Venkata V
VV
Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792-3252, USA.
Yu
Huanzhou
H
Pineda
Angel R
AR
McKenzie
Charles A
CA
Brittain
Jean H
JH
Reeder
Scott B
SB
D000273:Adipose Tissue / Q000473:pathology*; D000465:Algorithms; D016477:Artifacts; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009821:Oils / Q000737:chemistry; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014867:Water / Q000737:chemistry
D009821:Oils; D014867:Water