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Quantification in magnetic resonance spectroscopy based on semi-parametric approaches

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Abstract

Magnetic resonance spectroscopy (MRS) is a value-added modality to magnetic resonance imaging (MRI) that is often used in diagnosis, treatment and progression monitoring, as well as in non-destructive, non-invasive studies of disease states in humans and model systems in animals. The availability of high magnetic field strengths and use of hyperpolarized nuclei, combined with the possibility of acquiring spectra at very short echo-time, have dramatically increased the potential of MRS. For the last two decades, a challenge has been to quantify short echo-time proton spectra that exhibit many metabolites, and to estimate their concentrations. Quantification of such spectra is challenging. Because the model function describing the acquired MRS signal is incomplete, semi-parametric techniques are required for estimation of the wanted metabolite concentrations. The semi-parametric approaches, QUEST, AQSES, TARQUIN, LCModel and SiToolsFITT, are reviewed and discussed according to handling of macromolecule signal and unknown decay of the metabolite signal (lineshape). Estimation of noise-related errors on model parameters and compromise used in real-world applications are detailed, with emphasis on the bias-variance trade-off. Applications of the semi-parametric methods QUEST and AQSES to quantification of MRS, HRMAS and MRSI data are also provided.

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Abbreviations

AMARES:

Advanced Method for Accurate, Robust and Efficient Spectral fitting.

AQSES:

Automated Quantification of Short Echo Time MRS Signals

CRB:

Cramér–Rao Bound

LCModel:

Linear Combination of Models

NMR-SCOPE:

NMR Spectra Calculation using OPErators

MIDAS:

Metabolite Imaging and Data Analysis System

QUEST:

QUantification based on QUantum ESTimation

SVD:

Singular Value Decomposition

VARPRO:

VARiable PROjection

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Acknowledgments

This work was supported by the EU Marie Curie Research Network, FAST, MRTNCT-2006-035801 and Philips Medical Systems, Best, NL. The author warmly thanks Dr. Dirk van Ormondt for his fruitful collaboration and MSc Dan Stefan for his huge contribution to the jMRUI software package. She is also grateful to all persons involved in FAST and in the development of the algorithms and the GUI of the jMRUI software package. The author acknowledges Dr. Sylviane Confort-Gouny, CEMEREM-CRMBM, Marseille, France; Dr. Florence Fauvelle, CRSSA, Grenoble and Dr. Dominique Sappey-Marinier, CERMEP-Creatis-LRMN, Lyon, France for providing the MRS, HRMAS and MRSI data respectively.

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  1. Laboratoire CREATIS, CNRS UMR 5220, Inserm U1044, Université Claude Bernard LYON 1, 3 Rue Victor Grignard, CPE, 69616, Villeurbanne, France

    Danielle Graveron-Demilly

  2. INSA Lyon, Villeurbanne, France

    Danielle Graveron-Demilly

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Graveron-Demilly, D. Quantification in magnetic resonance spectroscopy based on semi-parametric approaches. Magn Reson Mater Phy 27, 113–130 (2014). https://doi.org/10.1007/s10334-013-0393-4

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