The Use of Continuous Regularization in the Automated Analysis of MRS Time-Domain Data |
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| Authors: | Jens Totz Aad van den Boogaart Sabine Van Huffel Danielle Graveron-Demilly Ioannis Dologlou Ralf Heidler Dieter Michel |
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| Institution: | aFakultät für Physik und Geowissenschaften, Universität Leipzig, Linnéstrasse 5, D-04103, Leipzig, Germany;bBiomedical NMR Unit, Department of Radiology, Faculty of Medicine, Katholieke Universiteit Leuven, Gasthuisberg, 3000, Leuven, Belgium;cDepartment of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kardinaal Mercierlaan 94, 3001, Leuven-Heverlee, Belgium;dLaboratoire de RMN, CNRS UPRESA 5012, Université Lyon I-CPE, 43 Boulevard du 11 Novembre 1918, 69622, Villeurbanne Cedex, France;eNational Technical University of Athens, 9 Iroon Polytechniou Street, Zographou, 15773, Greece |
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| Abstract: | In this paper, it is shown how the advantages of continuous regularization (CR) can be exploited to achieve an improved, fully automated LPSVD analysis of MRS time-domain data. The main advantage of CR is its ability to determine the number of spectral components even at low signal-to-noise ratios, which suggested its use forin vivospectroscopy. Estimation of the spectral parameters is possible. Two alternatives of automated data-analysis schemes are thoroughly investigated by means of Monte Carlo studies. The results suggest the combination of CR for model-order estimation with other methods for more-accurate parameter estimation. Several possible combinations, including those with an improved enhancement procedure and a total-least-squares method for quantitation, are discussed. Recommendations are given for spectral analysis, and a new data-analysis protocol which performs significantly better than previously used protocols of the same type is proposed. |
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