Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.

[1]  Charlotte J. Stagg,et al.  Magnetic Resonance Spectroscopy: Tools for Neuroscience Research and Emerging Clinical Applications , 2013 .

[2]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[3]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.

[4]  Sabine Van Huffel,et al.  Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Paulo J. G. Lisboa,et al.  Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours , 2012, BMC Bioinformatics.

[6]  Sabine Van Huffel,et al.  Hierarchical non‐negative matrix factorization to characterize brain tumor heterogeneity using multi‐parametric MRI , 2015, NMR in biomedicine.

[7]  M. Julià-Sapé,et al.  Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes , 2016, NMR in biomedicine.

[8]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[9]  Margarida Julià-Sapé,et al.  Prospective diagnostic performance evaluation of single‐voxel 1H MRS for typing and grading of brain tumours , 2012, NMR in biomedicine.

[10]  Margarida Julià-Sapé,et al.  Strategies for annotation and curation of translational databases: the eTUMOUR project , 2012, Database J. Biol. Databases Curation.

[11]  Jens Frahm,et al.  Clinical proton MR spectroscopy in central nervous system disorders. , 2013, Radiology.

[12]  Margarida Julià-Sapé,et al.  The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses , 2010, BMC Bioinformatics.

[13]  Carles Arús,et al.  Automated quality control protocol for MR spectra of brain tumors , 2008, Magnetic resonance in medicine.

[14]  Chris H. Q. Ding,et al.  Convex and Semi-Nonnegative Matrix Factorizations , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  R. Kreis.  Issues of spectral quality in clinical 1H‐magnetic resonance spectroscopy and a gallery of artifacts , 2004, NMR in biomedicine.

[16]  A. W. Simonetti,et al.  Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra , 2006, NMR in biomedicine.

[17]  M. Julià-Sapé,et al.  A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients , 2006, Magnetic Resonance Materials in Physics, Biology and Medicine.

[18]  Margarida Julià-Sapé,et al.  MRS quality assessment in a multicentre study on MRS‐based classification of brain tumours , 2008, NMR in biomedicine.

[19]  Margarida Julià-Sapé,et al.  SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system , 2010, BMC Bioinformatics.

[20]  A R Tate,et al.  Towards a method for automated classification of 1H MRS spectra from brain tumours , 1998, NMR in biomedicine.