Automated quality control of small animal MR neuroimaging data

Kalantari, Aref; Shahbazi, Mehrab; Schneider, Marc; Raikes, Adam C.; Frazão, Victor Vera; Bhattrai, Avnish; Carnevale, Lorenzo; Diao, Yujian; Franx, Bart A. A.; Gammaraccio, Francesco; Goncalves, Lisa Marie; Lee, Susan; Leeuwen, Esther M. van; Michalek, Annika; Mueller, Susanne; Rivera Olvera, Alejandro; Padro, Daniel; Kotb Selim, Mohamed; Toorn, Annette van der; Varriano, Federico; Vrooman, Roël; Wenk, Patricia; Albers, H. Elliott; Boehm Sturm, Philipp; Budinger, Eike; Canals, Santiago; Santis, Silvia de; Diaz Brinton, Roberta; Dijkhuizen, Rick M.; Eixarch Roca, Elisenda; Forloni, Gianluigi; Grandjean, Joanes; Hekmatyar, Khan; Jacobs, Russell E.; Jelescu, Ileana; Kurniawan, Nyoman D.; Lembo, Giuseppe; Longo, Dario Livio; Sta Maria, Naomi S.; Micotti, Edoardo; Muñoz Moreno, Emma; Ramos Cabrer, Pedro; Reichardt, Wilfried; Soria, Guadalupe; Ielacqua, Giovanna D.; Aswendt, Markus

Automated quality control of small animal MR neuroimaging data

Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge...

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Autores:	Kalantari, Aref, Shahbazi, Mehrab, Schneider, Marc, Raikes, Adam C., Frazão, Victor Vera, Bhattrai, Avnish, Carnevale, Lorenzo, Diao, Yujian, Franx, Bart A. A., Gammaraccio, Francesco, Goncalves, Lisa Marie, Lee, Susan, Leeuwen, Esther M. van, Michalek, Annika, Mueller, Susanne, Rivera Olvera, Alejandro, Padro, Daniel, Kotb Selim, Mohamed, Toorn, Annette van der, Varriano, Federico, Vrooman, Roël, Wenk, Patricia, Albers, H. Elliott, Boehm Sturm, Philipp, Budinger, Eike, Canals, Santiago, Santis, Silvia de, Diaz Brinton, Roberta, Dijkhuizen, Rick M., Eixarch Roca, Elisenda, Forloni, Gianluigi, Grandjean, Joanes, Hekmatyar, Khan, Jacobs, Russell E., Jelescu, Ileana, Kurniawan, Nyoman D., Lembo, Giuseppe, Longo, Dario Livio, Sta Maria, Naomi S., Micotti, Edoardo, Muñoz Moreno, Emma, Ramos Cabrer, Pedro, Reichardt, Wilfried, Soria, Guadalupe, Ielacqua, Giovanna D., Aswendt, Markus
Tipo de recurso:	artículo
Estado:	Versión publicada
Fecha de publicación:	2024
País:	España
Institución:	Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:	Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:	oai:recercat.cat:2445/223295
Acceso en línea:	https://hdl.handle.net/2445/223295
Access Level:	acceso abierto
Palabra clave:	Imatges per ressonància magnètica Neuroanatomia Mapatge del cervell Magnetic resonance imaging Neuroanatomy Brain mapping

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spelling	Automated quality control of small animal MR neuroimaging dataKalantari, ArefShahbazi, MehrabSchneider, MarcRaikes, Adam C.Frazão, Victor VeraBhattrai, AvnishCarnevale, LorenzoDiao, YujianFranx, Bart A. A.Gammaraccio, FrancescoGoncalves, Lisa MarieLee, SusanLeeuwen, Esther M. vanMichalek, AnnikaMueller, SusanneRivera Olvera, AlejandroPadro, DanielKotb Selim, MohamedToorn, Annette van derVarriano, FedericoVrooman, RoëlWenk, PatriciaAlbers, H. ElliottBoehm Sturm, PhilippBudinger, EikeCanals, SantiagoSantis, Silvia deDiaz Brinton, RobertaDijkhuizen, Rick M.Eixarch Roca, ElisendaForloni, GianluigiGrandjean, JoanesHekmatyar, KhanJacobs, Russell E.Jelescu, IleanaKurniawan, Nyoman D.Lembo, GiuseppeLongo, Dario LivioSta Maria, Naomi S.Micotti, EdoardoMuñoz Moreno, EmmaRamos Cabrer, PedroReichardt, WilfriedSoria, GuadalupeIelacqua, Giovanna D.Aswendt, MarkusImatges per ressonància magnèticaNeuroanatomiaMapatge del cervellMagnetic resonance imagingNeuroanatomyBrain mappingMagnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low-to-moderate concordance. In a manual post hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.The MIT Press2025202520242025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion23 p.application/pdfhttps://hdl.handle.net/2445/223295Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1162/imag_a_00317Imaging Neuroscience, 2024, vol. 2https://doi.org/10.1162/imag_a_00317cc-by (c) Kalantari, A. et al., 2024http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2232952026-05-29T05:05:01Z
dc.title.none.fl_str_mv	Automated quality control of small animal MR neuroimaging data
title	Automated quality control of small animal MR neuroimaging data
spellingShingle	Automated quality control of small animal MR neuroimaging data Kalantari, Aref Imatges per ressonància magnètica Neuroanatomia Mapatge del cervell Magnetic resonance imaging Neuroanatomy Brain mapping
title_short	Automated quality control of small animal MR neuroimaging data
title_full	Automated quality control of small animal MR neuroimaging data
title_fullStr	Automated quality control of small animal MR neuroimaging data
title_full_unstemmed	Automated quality control of small animal MR neuroimaging data
title_sort	Automated quality control of small animal MR neuroimaging data
dc.creator.none.fl_str_mv	Kalantari, Aref Shahbazi, Mehrab Schneider, Marc Raikes, Adam C. Frazão, Victor Vera Bhattrai, Avnish Carnevale, Lorenzo Diao, Yujian Franx, Bart A. A. Gammaraccio, Francesco Goncalves, Lisa Marie Lee, Susan Leeuwen, Esther M. van Michalek, Annika Mueller, Susanne Rivera Olvera, Alejandro Padro, Daniel Kotb Selim, Mohamed Toorn, Annette van der Varriano, Federico Vrooman, Roël Wenk, Patricia Albers, H. Elliott Boehm Sturm, Philipp Budinger, Eike Canals, Santiago Santis, Silvia de Diaz Brinton, Roberta Dijkhuizen, Rick M. Eixarch Roca, Elisenda Forloni, Gianluigi Grandjean, Joanes Hekmatyar, Khan Jacobs, Russell E. Jelescu, Ileana Kurniawan, Nyoman D. Lembo, Giuseppe Longo, Dario Livio Sta Maria, Naomi S. Micotti, Edoardo Muñoz Moreno, Emma Ramos Cabrer, Pedro Reichardt, Wilfried Soria, Guadalupe Ielacqua, Giovanna D. Aswendt, Markus
author	Kalantari, Aref
author_facet	Kalantari, Aref Shahbazi, Mehrab Schneider, Marc Raikes, Adam C. Frazão, Victor Vera Bhattrai, Avnish Carnevale, Lorenzo Diao, Yujian Franx, Bart A. A. Gammaraccio, Francesco Goncalves, Lisa Marie Lee, Susan Leeuwen, Esther M. van Michalek, Annika Mueller, Susanne Rivera Olvera, Alejandro Padro, Daniel Kotb Selim, Mohamed Toorn, Annette van der Varriano, Federico Vrooman, Roël Wenk, Patricia Albers, H. Elliott Boehm Sturm, Philipp Budinger, Eike Canals, Santiago Santis, Silvia de Diaz Brinton, Roberta Dijkhuizen, Rick M. Eixarch Roca, Elisenda Forloni, Gianluigi Grandjean, Joanes Hekmatyar, Khan Jacobs, Russell E. Jelescu, Ileana Kurniawan, Nyoman D. Lembo, Giuseppe Longo, Dario Livio Sta Maria, Naomi S. Micotti, Edoardo Muñoz Moreno, Emma Ramos Cabrer, Pedro Reichardt, Wilfried Soria, Guadalupe Ielacqua, Giovanna D. Aswendt, Markus
author_role	author
author2	Shahbazi, Mehrab Schneider, Marc Raikes, Adam C. Frazão, Victor Vera Bhattrai, Avnish Carnevale, Lorenzo Diao, Yujian Franx, Bart A. A. Gammaraccio, Francesco Goncalves, Lisa Marie Lee, Susan Leeuwen, Esther M. van Michalek, Annika Mueller, Susanne Rivera Olvera, Alejandro Padro, Daniel Kotb Selim, Mohamed Toorn, Annette van der Varriano, Federico Vrooman, Roël Wenk, Patricia Albers, H. Elliott Boehm Sturm, Philipp Budinger, Eike Canals, Santiago Santis, Silvia de Diaz Brinton, Roberta Dijkhuizen, Rick M. Eixarch Roca, Elisenda Forloni, Gianluigi Grandjean, Joanes Hekmatyar, Khan Jacobs, Russell E. Jelescu, Ileana Kurniawan, Nyoman D. Lembo, Giuseppe Longo, Dario Livio Sta Maria, Naomi S. Micotti, Edoardo Muñoz Moreno, Emma Ramos Cabrer, Pedro Reichardt, Wilfried Soria, Guadalupe Ielacqua, Giovanna D. Aswendt, Markus
author2_role	author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author
dc.subject.none.fl_str_mv	Imatges per ressonància magnètica Neuroanatomia Mapatge del cervell Magnetic resonance imaging Neuroanatomy Brain mapping
topic	Imatges per ressonància magnètica Neuroanatomia Mapatge del cervell Magnetic resonance imaging Neuroanatomy Brain mapping
description	Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low-to-moderate concordance. In a manual post hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.
publishDate	2024
dc.date.none.fl_str_mv	2024 2025 2025 2025
dc.type.none.fl_str_mv	info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion
format	article
status_str	publishedVersion
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2445/223295
url	https://hdl.handle.net/2445/223295
dc.language.none.fl_str_mv	Inglés
language_invalid_str_mv	Inglés
dc.relation.none.fl_str_mv	Reproducció del document publicat a: https://doi.org/10.1162/imag_a_00317 Imaging Neuroscience, 2024, vol. 2 https://doi.org/10.1162/imag_a_00317
dc.rights.none.fl_str_mv	cc-by (c) Kalantari, A. et al., 2024 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
rights_invalid_str_mv	cc-by (c) Kalantari, A. et al., 2024 http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	23 p. application/pdf
dc.publisher.none.fl_str_mv	The MIT Press
publisher.none.fl_str_mv	The MIT Press
dc.source.none.fl_str_mv	Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques) reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str	Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str	Recercat. Dipósit de la Recerca de Catalunya
collection	Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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Automated quality control of small animal MR neuroimaging data

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