Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the cur...

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Authors: Hernández Villegas, Yanisleydis, Ortega Martorell, Sandra, Arus Caraltó, Carles, Vellido Alcacena, Alfredo|||0000-0002-9843-1911, Julia Sape, Margarida
Format: article
Publication Date:2020
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/178175
Online Access:https://hdl.handle.net/2117/178175
https://dx.doi.org/10.1002/nbm.4193
Access Level:Open access
Keyword:Tumors -- Classification
Acquisition methods
Artifacts and corrections
Methods and engineering
MR spectrosocpy (MRS) and spectroscopic imaging (MRSI) methods
Post-acquisition processing
Tumors -- Classificació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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oai_identifier_str oai:upcommons.upc.edu:2117/178175
network_acronym_str ES
network_name_str España
repository_id_str
spelling Extraction of artefactual MRS patterns from a large database using non-negative matrix factorizationHernández Villegas, YanisleydisOrtega Martorell, SandraArus Caraltó, CarlesVellido Alcacena, Alfredo|||0000-0002-9843-1911Julia Sape, MargaridaTumors -- ClassificationAcquisition methodsArtifacts and correctionsMethods and engineeringMR spectrosocpy (MRS) and spectroscopic imaging (MRSI) methodsPost-acquisition processingTumors -- ClassificacióÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::BioinformàticaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialDespite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach.Peer Reviewed20222022-04-0120202020-02-20journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/178175https://dx.doi.org/10.1002/nbm.4193reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1781752026-05-27T15:37:01Z
dc.title.none.fl_str_mv Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
title Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
spellingShingle Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
Hernández Villegas, Yanisleydis
Tumors -- Classification
Acquisition methods
Artifacts and corrections
Methods and engineering
MR spectrosocpy (MRS) and spectroscopic imaging (MRSI) methods
Post-acquisition processing
Tumors -- Classificació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
title_full Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
title_fullStr Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
title_full_unstemmed Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
title_sort Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization
dc.creator.none.fl_str_mv Hernández Villegas, Yanisleydis
Ortega Martorell, Sandra
Arus Caraltó, Carles
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Julia Sape, Margarida
author Hernández Villegas, Yanisleydis
author_facet Hernández Villegas, Yanisleydis
Ortega Martorell, Sandra
Arus Caraltó, Carles
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Julia Sape, Margarida
author_role author
author2 Ortega Martorell, Sandra
Arus Caraltó, Carles
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Julia Sape, Margarida
author2_role author
author
author
author
dc.subject.none.fl_str_mv Tumors -- Classification
Acquisition methods
Artifacts and corrections
Methods and engineering
MR spectrosocpy (MRS) and spectroscopic imaging (MRSI) methods
Post-acquisition processing
Tumors -- Classificació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Tumors -- Classification
Acquisition methods
Artifacts and corrections
Methods and engineering
MR spectrosocpy (MRS) and spectroscopic imaging (MRSI) methods
Post-acquisition processing
Tumors -- Classificació
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro-oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert-based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition-based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non-Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H-MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-02-20
2022
2022-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/178175
https://dx.doi.org/10.1002/nbm.4193
url https://hdl.handle.net/2117/178175
https://dx.doi.org/10.1002/nbm.4193
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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