Summary
In this paper we discuss the problem of modeling Magnetic Resonance Spectroscopic Imaging (MRSI) signals, in the aim of estimating metabolite concentration over a region of the brain. To this end, we formulate nonconvex optimization problems and focus on appropriate constraints and starting values for the model parameters. Furthermore, we explore the applicability of spatial smoothness for the nonlinear model parameters across the MRSI grid. In order to simultaneously fit all signals in the grid and to impose spatial constraints, an adaptive alternating nonlinear least squares algorithm is proposed. This method is shown to be much more reliable than independently fitting each signal in the grid.
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Keywords
- Spatial Constraint
- Magnetic Resonance Spectroscopic Image
- Nonconvex Optimization Problem
- Spatial Smoothness
- Metabolite Spectrum
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References
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Acknowledgments
D.M. Sima is a postdoctoral fellow of the Fund for Scientific Research-Flanders. S. Van Huffel is a full professor at the Katholieke Universiteit Leuven, Belgium. Research supported by
• Research Council KUL: GOA-AMBioRICS, GOA MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IDO 05/010 EEG-fMRI, IDO 08/013 Autism, IOF-KP06/11 FunCop, several PhD/postdoc and fellow grants;
• Flemish Government:
– FWO: PhD/postdoc grants, projects, G.0519.06 (Noninvasive brain oxygenation), FWOG. 0321.06 (Tensors/Spectral Analysis), G.0302.07 (SVM), G.0341.07 (Data fusion), research communities (ICCoS, ANMMM);
– IWT: TBM070713-Accelero, TBM070706-IOTA3, PhD Grants;
• Belgian Federal Science Policy Office IUAP P6/04 (DYSCO, ‘Dynamical systems, control and optimization’, 2007–2011);
• EU: ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), Healthagents (IST200427214), FAST (FP6-MC-RTN-035801), Neuromath (COST-BM0601)
• ESA: Cardiovascular Control (Prodex-8 C90242)
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Sima, D.M., Sava, A.C., Van Huffel, S. (2010). Adaptive Alternating Minimization for Fitting Magnetic Resonance Spectroscopic Imaging Signals. In: Diehl, M., Glineur, F., Jarlebring, E., Michiels, W. (eds) Recent Advances in Optimization and its Applications in Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12598-0_45
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DOI: https://doi.org/10.1007/978-3-642-12598-0_45
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