Literature DB >> 10423356

Accurate quantification of (1)H spectra: from finite impulse response filter design for solvent suppression to parameter estimation.

T Sundin1, L Vanhamme, P Van Hecke, I Dologlou, S Van Huffel.   

Abstract

A scheme for accurate quantification of (1)H spectra is presented. The method uses maximum-phase finite impulse response (FIR) filters for solvent suppression and an iterative nonlinear least-squares (NLLS) algorithm for parameter estimation. The estimation algorithm takes the filter influence on the metabolites of interest into account and can thereby correctly incorporate a large variety of prior knowledge into the estimation phase. The FIR filter is designed in such a way that no distortion of the important initial samples is introduced. The FIR filter method is compared numerically with the HSVD method for water signal removal in a number of examples. The results show that the FIR method, using an automatic filter design scheme, slightly outperforms the HSVD method in most cases. The good performance and ease of use of the FIR filter method combined with its low computational complexity motivate the use of the proposed method. Copyright 1999 Academic Press.

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Year:  1999        PMID: 10423356     DOI: 10.1006/jmre.1999.1782

Source DB:  PubMed          Journal:  J Magn Reson        ISSN: 1090-7807            Impact factor:   2.229


  7 in total

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Authors:  Wilhelm M Malloni; Silvia De Sanctis; Ana M Tomé; Elmar W Lang; Claudia E Munte; Klaus Peter Neidig; Hans Robert Kalbitzer
Journal:  J Biomol NMR       Date:  2010-04-23       Impact factor: 2.835

2.  Water removal in MR spectroscopic imaging with L2 regularization.

Authors:  Liangjie Lin; Michal Považan; Adam Berrington; Zhong Chen; Peter B Barker
Journal:  Magn Reson Med       Date:  2019-05-31       Impact factor: 4.668

3.  Java-based graphical user interface for the MRUI quantitation package.

Authors:  A Naressi; C Couturier; J M Devos; M Janssen; C Mangeat; R de Beer; D Graveron-Demilly
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4.  Decimative spectral estimation with unconstrained model order.

Authors:  Stavroula-Evita Fotinea; Ioannis Dologlou; Stylianos Bakamidis; Theologos Athanaselis
Journal:  Comput Math Methods Med       Date:  2012-02-22       Impact factor: 2.238

5.  Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

Authors:  Adrian Ion-Mărgineanu; Gabriel Kocevar; Claudio Stamile; Diana M Sima; Françoise Durand-Dubief; Sabine Van Huffel; Dominique Sappey-Marinier
Journal:  Front Neurosci       Date:  2017-07-11       Impact factor: 4.677

6.  Designing GABA-edited magnetic resonance spectroscopy studies: Considerations of scan duration, signal-to-noise ratio and sample size.

Authors:  Mark Mikkelsen; Rachelle S Loo; Nicolaas A J Puts; Richard A E Edden; Ashley D Harris
Journal:  J Neurosci Methods       Date:  2018-02-21       Impact factor: 2.987

7.  Application of ICA to realistically simulated (1)H-MRS data.

Authors:  Ravi Kalyanam; David Boutte; Kent E Hutchison; Vince D Calhoun
Journal:  Brain Behav       Date:  2015-04-25       Impact factor: 2.708

  7 in total

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