title: Noise considerations of three-point water-fat separation imaging methods.
abstract: Separation of water from fat tissues in magnetic resonance imaging is important for many applications because signals from fat tissues often interfere with diagnoses that are usually based on water signal characteristics. Water and fat can be separated with images acquired at different echo time shifts. The three-point method solves for the unknown off-resonance frequency together with the water and fat densities. Noise performance of the method, quantified by the effective number of signals averaged (NSA), is an important metric of the water and fat images. The authors use error propagation theory and Monte Carlo simulation to investigate two common reconstructive approaches: an analytic-solution based estimation and a least-squares estimation. Two water-fat chemical shift (CS) encoding strategies, the symmetric (-theta, 0, theta) and the shifted (0, theta, 2theta) schemes are studied and compared. Results show that NSAs of water and fat can be different and they are dependent on the ratio of intensities of the two species and each of the echo time shifts. The NSA is particularly poor for the symmetric (-theta, 0, theta) CS encoding when the water and fat signals are comparable. This anomaly with equal amounts of water and fat is analyzed in a more intuitive geometric illustration. Theoretical prediction of NSA matches well with simulation results at high signal-to-noise ratio (SNR), while deviation arises at low SNR, which suggests that Monte Carlo simulation may be more appropriate to accurately predict noise performance of the algorithm when SNR is low.
authors.affiliation: Department of Physics, Stanford University, Stanford, California 94305, USA. wenzf@stanfordalumni.org
authors.forename: Zhifei
authors.initials: Z
authors.lastname: Wen
authors.forename: Scott B
authors.initials: SB
authors.lastname: Reeder
authors.forename: Angel R
authors.initials: AR
authors.lastname: Pineda
authors.forename: Norbert J
authors.initials: NJ
authors.lastname: Pelc
mesh_terms: D000273:Adipose Tissue / Q000033:anatomy & histology*; D016477:Artifacts; D001834:Body Water; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009010:Monte Carlo Method; D009622:Noise
references.citation: J Magn Reson Imaging. 1991 Sep-Oct;1(5):521-30
references.pmid: 1790376
references.citation: Magn Reson Med. 2004 Jan;51(1):35-45
references.pmid: 14705043
references.citation: Phys Med Biol. 2005 Aug 21;50(16):3697-715
references.pmid: 16077222
references.citation: Magn Reson Med. 2002 Dec;48(6):1021-7
references.pmid: 12465112
references.citation: Radiology. 1984 Oct;153(1):189-94
references.pmid: 6089263
references.citation: Med Phys. 1999 Nov;26(11):2341-7
references.pmid: 10587215
references.citation: Magn Reson Med. 2005 Sep;54(3):625-35
references.pmid: 16092102
references.citation: Magn Reson Med. 2005 Sep;54(3):636-44
references.pmid: 16092103
references.citation: J Magn Reson Imaging. 1997 Nov-Dec;7(6):1002-15
references.pmid: 9400843
references.citation: Magn Reson Med. 1991 Apr;18(2):371-83
references.pmid: 2046518
references.citation: Med Phys. 1985 Sep-Oct;12(5):586-91
references.pmid: 4046992
references.citation: Magn Reson Med. 2006 Sep;56(3):572-84
references.pmid: 16894578
references.citation: IEEE Trans Med Imaging. 2002 Aug;21(8):966-77
references.pmid: 12472269
references.citation: Magn Reson Med. 2005 Oct;54(4):1032-9
references.pmid: 16142718