title: Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification.
abstract: Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.|University of Toulouse, IRIT - INP-ENSEEIHT, Toulouse, France.
authors.forename: Andrea
authors.initials: A
authors.lastname: Laruelo
authors.affiliation: University of Toulouse, IRIT - INP-ENSEEIHT, Toulouse, France.|MIRACL Laboratory, Sfax, Tunisia.
authors.forename: Lotfi
authors.initials: L
authors.lastname: Chaari
authors.affiliation: University of Toulouse, IRIT - INP-ENSEEIHT, Toulouse, France.
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Tourneret
authors.affiliation: University of Toulouse, IRIT - INP-ENSEEIHT, Toulouse, France.
authors.forename: Hadj
authors.initials: H
authors.lastname: Batatia
authors.affiliation: Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.|INSERM UMR1214 TONIC, Toulouse, France.
authors.forename: Soléakhéna
authors.initials: S
authors.lastname: Ken
authors.affiliation: Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.
authors.forename: Ben
authors.initials: B
authors.lastname: Rowland
authors.affiliation: Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.
authors.forename: Régis
authors.initials: R
authors.lastname: Ferrand
authors.affiliation: Institut Claudius Regaud, Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France.|Université Toulouse III Paul Sabatier, Toulouse, France.|INSERM UMR1214 TONIC, Toulouse, France.
authors.forename: Anne
authors.initials: A
authors.lastname: Laprie
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism; D001921:Brain / Q000378:metabolism*; D001932:Brain Neoplasms / Q000378:metabolism*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D057054:Molecular Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D062211:Spatio-Temporal Analysis
chemical_list: D014408:Biomarkers, Tumor
keywords: MRSI; quantification; spatial spectral prior knowledge