OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Laruelo, Andrea and Chaari, Lotfi and Tourneret, Jean-Yves and Batatia, Hadj and Ken, Soleakhena and Rowland, Ben and Ferrand, Régis and Laprie, Anne Spatio-spectral regularization to improve magnetic resonance spectroscopic imaging quantification. (2016) NMR in Biomedicine, vol. 129 (n° 7). pp. 918-931. ISSN 0952-3480

(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader

Official URL: http://dx.doi.org/10.1002/nbm.3532


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!t`e 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.

Item Type:Article
Additional Information:Thanks to Wiley editor . The definitive version is available at http://onlinelibrary.wiley.com/ The original PDF of the article can be found at http://onlinelibrary.wiley.com/doi/10.1002/nbm.3532/full
HAL Id:hal-01381730
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
French research institutions > Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Institut Claudius Regaud - ICR (FRANCE)
Laboratory name:
Deposited By: Jean-yves TOURNERET
Deposited On:30 Sep 2016 07:38

Repository Staff Only: item control page