Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Autor: Laruelo, Andrea, Chaari, Lotfi, Tourneret, Jean-Yves, Batatia, Hadj, Ken, Soleakhena, Rowland, Ben, Ferrand, Régis, Laprie, Anne
Přispěvatelé: Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Institut Claudius Regaud - ICR (FRANCE), Traitement et Compréhension d’Images (IRIT-TCI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut Claudius Regaud, Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Institut National Polytechnique (Toulouse) (Toulouse INP), CoMputational imagINg anD viSion (IRIT-MINDS), Institut National de la Santé et de la Recherche Médicale (INSERM), Part of the SUMMER Marie Curie Research Training Network (PITN-GA-2011-290148), which is funded by the 7th Framework Programme of the European Commission (FP7-PEOPLE-2011-ITN), ANR-11-IDEX-0002,UNITI,Université Fédérale de Toulouse(2011), ANR-11-LABX-0040,CIMI,Centre International de Mathématiques et d'Informatique (de Toulouse)(2011), (OATAO), Open Archive Toulouse Archive Ouverte, Initiative d'excellence - Université Fédérale de Toulouse - - UNITI2011 - ANR-11-IDEX-0002 - IDEX - VALID, Centre International de Mathématiques et d'Informatique (de Toulouse) - - CIMI2011 - ANR-11-LABX-0040 - LABX - VALID, Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse Capitole (UT Capitole), Part of the SUMMER Marie Curie Research Training Network (PITN-GA-2011-290148) which is funded by the 7th Framework Programme of the European Commission (FP7-PEOPLE-2011-ITN)
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: NMR in Biomedicine
NMR in Biomedicine, Wiley, 2016, vol. 129 (n° 7), pp. 918-931. ⟨10.1002/nbm.3532⟩
NMR in Biomedicine, Wiley, 2016, 129 (7), pp.918-931. ⟨10.1002/nbm.3532⟩
NMR in Biomedicine, 2016, 129 (7), pp.918-931. ⟨10.1002/nbm.3532⟩
ISSN: 0952-3480
1099-1492
DOI: 10.1002/nbm.3532⟩
Popis: International audience; Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribu- tion 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 require- ment 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 pres- ence 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 quan- tify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization al- gorithm 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.
Databáze: OpenAIRE
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