A. Laprie, I. Catalaa, E. Cassol, T. Mcknight, D. Berchery et al., Proton Magnetic Resonance Spectroscopic Imaging in Newly Diagnosed Glioblastoma: Predictive Value for the Site of Postradiotherapy Relapse in a Prospective Longitudinal Study, International Journal of Radiation Oncology*Biology*Physics, vol.70, issue.3, pp.773-781, 2008.
DOI : 10.1016/j.ijrobp.2007.10.039

A. Deviers, S. Ken, T. Filleron, R. B. Laruelo, A. Catalaa et al., Evaluation of the Lactate-to-N-Acetyl-aspartate Ratio Defined With Magnetic Resonance Spectroscopic Imaging Before Radiation Therapy as a New Predictive Marker of the Site of Relapse in Patients With Glioblastoma Multiforme, International Journal of Radiation Oncology*Biology*Physics, vol.90, issue.2, pp.385-393, 2014.
DOI : 10.1016/j.ijrobp.2014.06.009

L. Kwock, J. Smith, M. Castillo, M. Ewend, F. Collichio et al., Clinical role of proton magnetic resonance spectroscopy in oncology: brain, breast, and prostate cancer, The Lancet Oncology, vol.7, issue.10, pp.859-868, 2006.
DOI : 10.1016/S1470-2045(06)70905-6

S. Posse, R. Otazo, S. Dager, and J. Alger, MR spectroscopic imaging: Principles and recent advances, Journal of Magnetic Resonance Imaging, vol.65, issue.6, pp.1301-1325, 2013.
DOI : 10.1002/jmri.23945

L. Vanhamme, . Van-den, A. Boogaart, and S. Van-huffel, Improved Method for Accurate and Efficient Quantification of MRS Data with Use of Prior Knowledge, Journal of Magnetic Resonance, vol.129, issue.1, pp.35-43, 1997.
DOI : 10.1006/jmre.1997.1244

B. Soher, K. Young, V. Govindaraju, and A. Maudsley, Automated spectral analysis III: Application toin Vivo proton MR Spectroscopy and spectroscopic imaging, Magnetic Resonance in Medicine, vol.30, issue.6, pp.822-831, 1998.
DOI : 10.1002/mrm.1910400607

S. Provencher, Automatic quantitation of localizedin vivo1H spectra with LCModel, NMR in Biomedicine, vol.18, issue.4, pp.260-264, 2001.
DOI : 10.1002/nbm.698

H. Ratiney, M. Sdika, Y. Coenradie, S. Cavassila, D. Van-ordmont et al., Time-domain semi-parametric estimation based on a metabolite basis set, NMR in Biomedicine, vol.13, issue.1, pp.1-13, 2005.
DOI : 10.1002/nbm.895
URL : https://hal.archives-ouvertes.fr/hal-00443422

J. Poullet, D. Sima, A. Simonetti, D. Neuter, B. Vanhamme et al., An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES, NMR in Biomedicine, vol.157, issue.5, pp.493-504, 2006.
DOI : 10.1002/nbm.1112

A. Sava, D. Sima, J. Poullet, A. Wright, A. Heerschap et al., Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions, NMR in Biomedicine, vol.29, issue.6, pp.824-835, 2011.
DOI : 10.1002/nbm.1628

B. Kelm, F. Kaster, A. Henning, M. Weber, P. Bachert et al., Using spatial prior knowledge in the spectral fitting of MRS images, NMR in Biomedicine, vol.49, issue.1, pp.1-13, 2012.
DOI : 10.1002/nbm.1704
URL : https://hal.archives-ouvertes.fr/inria-00616193

V. Boer, D. Klomp, C. Juchem, P. Luijten, and R. De-graaf, Multislice 1H MRSI of the human brain at 7 T using dynamic B0 and B1 shimming, Magnetic Resonance in Medicine, vol.21, issue.3, pp.662-670, 2012.
DOI : 10.1002/mrm.23288

A. Diop, Y. Zaim-wadghiri, A. Briguet, and D. Graveron-demilly, Improvements of Quantitation by Using the Cadzow Enhancement Procedure Prior to Any Linear-Prediction Methods, Journal of Magnetic Resonance, Series B, vol.105, issue.1, pp.17-24, 1994.
DOI : 10.1006/jmrb.1994.1094

Y. Bao and A. Maudsley, Improved reconstruction for MR spectroscopic imaging, IEEE Trans. Med. Imag, vol.26, pp.686-695, 2007.

H. Nguyen, J. Haldar, M. Do, and L. Zhi-pei, Denoising of MR spectroscopic imaging data with spatial-spectral regularization, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.720-723
DOI : 10.1109/ISBI.2010.5490073

A. Laruelo, L. Chaari, H. Batatia, S. Ken, R. B. Laprie et al., Hybrid sparse regularization for magnetic resonance spectroscopy, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.6768-71
DOI : 10.1109/EMBC.2013.6611110
URL : https://hal.archives-ouvertes.fr/hal-01148277

S. Cavassila, S. Deval, C. Huegen, D. Van-ormondt, and D. Graveron-demilly, Cram??r-Rao bounds: an evaluation tool for quantitation, NMR in Biomedicine, vol.59, issue.2, pp.278-283, 2001.
DOI : 10.1002/nbm.701

S. Mierisová, A. Van-den-boogaart, I. Tkác, P. Van-hecke, L. Vanhamme et al., New approach for quantitation of short echo time in vivo 1 H MR spectra of brain using AMARES, NMR Biomed, vol.1, pp.32-39, 1998.

S. Smith, T. Levante, B. Meier, and R. Ernst, Computer Simulations in Magnetic Resonance. An Object-Oriented Programming Approach, Journal of Magnetic Resonance, Series A, vol.106, issue.1, pp.75-105, 1994.
DOI : 10.1006/jmra.1994.1008

D. Graveron-demilly, A. Diop, A. Briguet, and B. Fenet, Product-Operator Algebra for Strongly Coupled Spin Systems, Journal of Magnetic Resonance, Series A, vol.101, issue.3, pp.233-239, 1993.
DOI : 10.1006/jmra.1993.1038

H. Serrai, L. Senhadji, J. De-certaines, and J. Coatrieux, Time-Domain Quantification of Amplitude, Chemical Shift, Apparent Relaxation TimeT*2, and Phase by Wavelet-Transform Analysis. Application to Biomedical Magnetic Resonance Spectroscopy, Journal of Magnetic Resonance, vol.124, issue.1, pp.20-34, 1997.
DOI : 10.1006/jmre.1996.7492

H. Serrai, L. Nadal-desbarats, H. Poptani, J. Glickson, and L. Senhadji, Lactate editing and lipid suppression by continuous wavelet transform analysis: Application to simulated and1H MRS brain tumor time-domain data, Magnetic Resonance in Medicine, vol.35, issue.5, pp.649-656, 2000.
DOI : 10.1002/(SICI)1522-2594(200005)43:5<649::AID-MRM6>3.0.CO;2-#

H. Cancino-de-greiff, R. Ramos-garcia, and J. Lorenzo-ginori, Signal de-noising in magnetic resonance spectroscopy using wavelet transforms, Concepts in Magnetic Resonance, vol.36, issue.6, pp.388-401, 2002.
DOI : 10.1002/cmr.10043

S. Zarei, J. Alireazie, P. Babyn, A. Kassner, and E. Widjaja, MRS feature extraction: time-frequency and wavelet analysis, Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering ICBBE, pp.1863-1866, 2008.

S. Hu, M. Lustig, A. Chen, J. Crane, A. Kerr et al., Compressed sensing for resolution enhancement of hyperpolarized 13C flyback 3D-MRSI, Journal of Magnetic Resonance, vol.192, issue.2, pp.258-264, 2008.
DOI : 10.1016/j.jmr.2008.03.003

S. Geethanath, H. Baek, S. Ganji, Y. Ding, E. Maher et al., H MR Metabolic Imaging in the Clinic, Radiology, vol.262, issue.3, pp.985-994, 2012.
DOI : 10.1148/radiol.11111098

L. Vanhamme, T. Sundin, P. Van-hecke, V. Huffel, and S. , MR spectroscopy quantitation: a review of time-domain methods, NMR in Biomedicine, vol.111, issue.4, pp.233-246, 2001.
DOI : 10.1002/nbm.695

A. Tikhonov, Tikhonov regularization of incorrectly posed problems, Sov. Math. Dokl, vol.4, pp.1624-1627, 1963.

L. Ying, D. Xu, and Z. Liang, On Tikhonov regularization for image reconstruction in parallel MRI, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1056-1059, 2004.
DOI : 10.1109/IEMBS.2004.1403345

M. Guerquin-kern, D. Van-de-ville, C. Vonesch, J. Baritaux, K. Pruessmann et al., Wavelet-regularized reconstruction for rapid MRI, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.193-196
DOI : 10.1109/ISBI.2009.5193016

L. Chaari, J. Pesquet, A. Benazza-benyahia, and P. Ciuciu, A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging???, Medical Image Analysis, vol.15, issue.2, pp.185-201, 2011.
DOI : 10.1016/j.media.2010.08.001
URL : https://hal.archives-ouvertes.fr/hal-00692260

L. Chaari, P. Ciuciu, S. Mériaux, and J. Pesquet, Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI, Magnetic Resonance Materials in Physics, Biology and Medicine, vol.1, issue.6, pp.509-529
DOI : 10.1007/s10334-014-0436-5
URL : https://hal.archives-ouvertes.fr/hal-01084324

P. Combettes and J. Pesquet, A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol.24, issue.6, 2008.
DOI : 10.1088/0266-5611/24/6/065014
URL : https://hal.archives-ouvertes.fr/hal-00692901

D. Stefan, D. Cesare, F. Andrasescu, A. Popa, E. Lazariev et al., Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package, Measurement Science and Technology, vol.20, issue.10, pp.104-139, 2009.
DOI : 10.1088/0957-0233/20/10/104035

J. Poullet, D. Sima, J. Luts, O. Garcia, M. Croitor et al., Simulation Package based in, In vitro Databases (SPID)ManualSPID. pdf, 2010.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

M. Belge, M. Kilmer, and E. Miller, Efficient determination of multiple regularization parameters in a generalized L-curve framework, Inverse Problems, vol.18, issue.4, pp.1161-1183, 2002.
DOI : 10.1088/0266-5611/18/4/314

L. Chaari, J. Pesquet, J. Tourneret, P. Ciuciu, and A. Benazza-benyahia, A hierarchical Bayesian model for frame representation, IEEE Trans. Signal Process, vol.18, issue.11, pp.5560-5571, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00853350

T. Laudadio, N. Mastronardi, L. Vanhamme, P. Van-hecke, V. Huffel et al., Improved Lanczos Algorithms for Blackbox MRS Data Quantitation, Journal of Magnetic Resonance, vol.157, issue.2, pp.292-297, 2002.
DOI : 10.1006/jmre.2002.2593

R. Kreis, Issues of spectral quality in clinical1H-magnetic resonance spectroscopy and a gallery of artifacts, NMR in Biomedicine, vol.17, issue.6, pp.361-381, 2004.
DOI : 10.1002/nbm.891

S. Ken, L. Vieillevigne, X. Franceries, L. Simon, C. Supper et al., Integration method of 3D MR spectroscopy into treatment planning system for glioblastoma IMRT dose painting with integrated simultaneous boost, Radiation Oncology, vol.8, issue.1, p.1, 2013.
DOI : 10.1016/S1474-4422(09)70204-2
URL : https://hal.archives-ouvertes.fr/inserm-00780170

P. Combettes, J. Pesquet, H. Bauschke, R. Burachik, P. Combettes et al., Proximal splitting methods in signal processing In Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2011.