Different quantification algorithms may lead to different results: a comparison using proton MRS lipid signals.
Proton magnetic resonance spectroscopy (MRS) is a sensitive method for investigating the biochemical compounds in a tissue. The interpretation of the data relies on the quantification algorithms applied to MR spectra. Each of these algorithms has certain underlying assumptions and may allow one to incorporate prior knowledge, which could influence the quality of the fit. The most commonly considered types of prior knowledge include the line-shape model (Lorentzian, Gaussian, Voigt), knowledge of the resonating frequencies, modeling of the baseline, constraints on the damping factors and phase, etc. In this article, we study whether the statistical outcome of a biological investigation can be influenced by the quantification method used. We chose to study lipid signals because of their emerging role in the investigation of metabolic disorders. Lipid spectra, in particular, are characterized by peaks that are in most cases not Lorentzian, because measurements are often performed in difficult body locations, e.g. in visceral fats close to peristaltic movements in humans or very small areas close to different tissues in animals. This leads to spectra with several peak distortions. Linear combination of Model spectra (LCModel), Advanced Method for Accurate Robust and Efficient Spectral fitting (AMARES), quantitation based on QUantum ESTimation (QUEST), Automated Quantification of Short Echo-time MRS (AQSES)-Lineshape and Integration were applied to simulated spectra, and area under the curve (AUC) values, which are proportional to the quantity of the resonating molecules in the tissue, were compared with true values. A comparison between techniques was also carried out on lipid signals from obese and lean Zucker rats, for which the polyunsaturation value expressed in white adipose tissue should be statistically different, as confirmed by high-resolution NMR measurements (considered the gold standard) on the same animals. LCModel, AQSES-Lineshape, QUEST and Integration gave the best results in at least one of the considered groups of simulated or in vivo lipid signals. These outcomes highlight the fact that quantification methods can influence the final result and its statistical significance.
Department of Computer Science, University of Verona, Verona, Italy.
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E
Mosconi
D M
DM
Sima
M I
MI
Osorio Garcia
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M
Fontanella
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S
Fiorini
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Van Huffel
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P
Marzola
D052436:Adipose Tissue, White / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D008055:Lipids / Q000737:chemistry*; D009682:Magnetic Resonance Spectroscopy; D009821:Oils / Q000737:chemistry; D011522:Protons; D051381:Rats; D011924:Rats, Zucker; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio
D008055:Lipids; D009821:Oils; D011522:Protons
AMARES; AQSES; LCModel; QUEST; lipids; magnetic resonance spectroscopy; quantitation; simulation