Quantitation

jMRUI includes several quantitation algorithms. Some of them are listed below:

SVD based algorithms

The non-interactive black box algorithms based on Singular Value Decomposition (SVD) are efficient for quantitating signals with good signal-to-noise ratios. They are also helpful in parametrizing signals of unknown composition and shape, but they cannot make use of all available prior knowledge.

HLSVD

The Hankel Lanczos Squares Singular Values Decomposition (HLSVD) is a very fast black box quantitation method based on the Lanczos algorithm.

HTLS

The Hankel Lanczos Total Least Squares (HLTLS) algorithm allows 2048 points for the quantitation of up to 50 components. If the first points of the signal are orders of magnitude larger than the other points, the algorithm might not be able to produce useful results. Truncation is a solution in this case.

LPSVD

The Linear Prediction Singular Value Decomposition (LPSVD) quantitation method is not very quick, but allows up to 99 components in the estimated signal. The number of points allowed in the quantitation is limited to 1024. This method is less robust than HLSVD and if LPSVD yields incoherent results, try to truncate some points at the signal beginning.

AMARES

AMARES  is a non-linear-least-squares (NLLS) quantitation algorithm that demands more user interaction compared to the black box methods. The user can provide constraints to some components and introduce some Prior Knowledge that can improve the precision of the quantitation.

Picture4
AMARES: 31P muscle kinetic effort, 2 T, quantification results.
amares1
Selection of starting values

 

AMARES: 31 P muscle kinetic effort, 2T, time evolution of metabolite amplitudes
AMARES: 31P muscle kinetic effort, 2 T, time evolution of metabolite amplitudes.

References

  • AMARES – Vanhamme, L., van den Boogaart, A., Van Huffel, S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. Journal of Magnetic Resonance 129: 35-43, 1997.
  • Danielle Graveron-Demilly. Quantification in magnetic resonance spectroscopy based on semi-parametric approaches. Magnetic Resonance Materials in Physics, Biology and Medicine, pages 1– 8, 2013. ISSN 0968-5243. doi: 10.1007/s10334-013-0393-4. URL http: //dx.doi.org/10.1007/s10334-013-0393-4

QUEST

QUEST is a time domain algorithm which fits a weighted combination of metabolite signals directly to the in vivo data at hand. It uses advanced prior knowledge based on a metabolite basis set either computed by Quantum Mechanics with NMR-SCOPE or NMRScopeB, or measured in vitro.

quest1
QUEST: Quantitation procedure start window
QUEST: human brain, 3T, TE = 30 ms
QUEST: human brain, 3 T, TE = 30 ms
jMRUI v. 5.0 - Human brain MRSI quantitation
QUEST: Quantitation of human-brain MRSI. The spectra were acquired at 1.5 T with TE of 150 ms. Due to the long TE this data set retained only a few peaks that were fitted in time-domain with QUEST using a simulated basis set of 3 metabolites: N-acetylaspartate, creatine and choline.

References

  • Danielle Graveron-Demilly. Quantification in magnetic resonance spectroscopy based on semi-parametric approaches. Magnetic Resonance Materials in Physics, Biology and Medicine, pages 1– 8, 2013. ISSN 0968-5243. doi: 10.1007/s10334-013-0393-4. URL http: //dx.doi.org/10.1007/s10334-013-0393-4

AQSES

AQSES is an algorithm  in which the quantitation problem is mathematically formulated as a separable non-linear least-squares fitting problem, which is numerically solved using a modified variable-projection procedure. A macromolecular baseline is incorporated into the fit via non-parametric modelling and is efficiently implemented using penalized splines. Unwanted components such as residual water are removed with a maximum-phase FIR filter. Constraints on the phases, dampings and frequencies of the metabolites can be imposed.

asques1

AQSES: human brain, 3T, TE = 30 ms
AQSES: human brain, 3 T, TE = 30 ms

References

  • M. Osorio Garcia, Diana Sima, F. Nielsen, U. Himmelreich and S. Van Huffel,  Quantification of in vivo Magnetic Resonance Spectroscopy signals with baseline and lineshape corrections
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