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A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation
Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re‐identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interop...
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| Published in: | Human brain mapping 2021-12, Vol.42 (17), p.5523-5534 |
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| container_title | Human brain mapping |
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| creator | Mikulan, Ezequiel Russo, Simone Zauli, Flavia Maria d'Orio, Piergiorgio Parmigiani, Sara Favaro, Jacopo Knight, William Squarza, Silvia Perri, Pierluigi Cardinale, Francesco Avanzini, Pietro Pigorini, Andrea |
| description | Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re‐identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user‐friendly 3D Slicer plugin‐in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state‐of‐the‐art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re‐identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user‐friendly interface, multiple input–output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.
In this article we present a novel MRI de‐identification method and perform a comparison of its performance with respect to other two state‐of‐the‐art methods. We show that our method performs similarly in terms of de‐identification but better preserves the geometrical properties of the images. It is open‐source and also includes an easy to use graphical user interface. |
| doi_str_mv | 10.1002/hbm.25639 |
| format | article |
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In this article we present a novel MRI de‐identification method and perform a comparison of its performance with respect to other two state‐of‐the‐art methods. We show that our method performs similarly in terms of de‐identification but better preserves the geometrical properties of the images. It is open‐source and also includes an easy to use graphical user interface.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.25639</identifier><identifier>PMID: 34520074</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Batch processing ; Brain research ; Comparative studies ; Confidentiality ; data sharing ; General Data Protection Regulation ; geometrical preservation ; Identification ; Information sharing ; Interoperability ; Legislation ; Localization ; Magnetic resonance imaging ; Methods ; MRI deidentification ; Neurosciences ; Performance evaluation ; Preservation ; Privacy ; Quality assurance ; Risk management ; Risk reduction</subject><ispartof>Human brain mapping, 2021-12, Vol.42 (17), p.5523-5534</ispartof><rights>2021 The Authors. published by Wiley Periodicals LLC.</rights><rights>2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4439-4cfccdcd20620504fb5d097cd7ea8e3ae5fb990922a6a612f295c88dc5facea43</citedby><cites>FETCH-LOGICAL-c4439-4cfccdcd20620504fb5d097cd7ea8e3ae5fb990922a6a612f295c88dc5facea43</cites><orcidid>0000-0001-7259-6120</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fhbm.25639$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhbm.25639$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,315,733,786,790,891,27957,27958,37047,37048,50923,51032,53827,53829</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34520074$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mikulan, Ezequiel</creatorcontrib><creatorcontrib>Russo, Simone</creatorcontrib><creatorcontrib>Zauli, Flavia Maria</creatorcontrib><creatorcontrib>d'Orio, Piergiorgio</creatorcontrib><creatorcontrib>Parmigiani, Sara</creatorcontrib><creatorcontrib>Favaro, Jacopo</creatorcontrib><creatorcontrib>Knight, William</creatorcontrib><creatorcontrib>Squarza, Silvia</creatorcontrib><creatorcontrib>Perri, Pierluigi</creatorcontrib><creatorcontrib>Cardinale, Francesco</creatorcontrib><creatorcontrib>Avanzini, Pietro</creatorcontrib><creatorcontrib>Pigorini, Andrea</creatorcontrib><title>A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re‐identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user‐friendly 3D Slicer plugin‐in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state‐of‐the‐art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re‐identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user‐friendly interface, multiple input–output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.
In this article we present a novel MRI de‐identification method and perform a comparison of its performance with respect to other two state‐of‐the‐art methods. We show that our method performs similarly in terms of de‐identification but better preserves the geometrical properties of the images. It is open‐source and also includes an easy to use graphical user interface.</description><subject>Algorithms</subject><subject>Batch processing</subject><subject>Brain research</subject><subject>Comparative studies</subject><subject>Confidentiality</subject><subject>data sharing</subject><subject>General Data Protection Regulation</subject><subject>geometrical preservation</subject><subject>Identification</subject><subject>Information sharing</subject><subject>Interoperability</subject><subject>Legislation</subject><subject>Localization</subject><subject>Magnetic resonance imaging</subject><subject>Methods</subject><subject>MRI deidentification</subject><subject>Neurosciences</subject><subject>Performance evaluation</subject><subject>Preservation</subject><subject>Privacy</subject><subject>Quality assurance</subject><subject>Risk management</subject><subject>Risk reduction</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1ks1u1DAQgCMEoj9w4AWQJS4gkdZ24iS-IC0V0JW6QkJwthx7suuS2Fvb2dXe-gi8B2_Fk-B0ywoqcfGP5ptPY89k2QuCzwjG9HzVDmeUVQV_lB0TzOscE148ns4Vy3lZk6PsJIRrjAlhmDzNjoqSUYzr8jj7OUPKDWvpZTQbQCGOeodaiFsAm24ywq_bH65LS1xNR-kjWnyZIw1Gg42mMyplOouk1Whmnd0t5m-RRBa2aIC4cnryt8Yau0R-MjzI8yZ8TwE9qoNmCS6l-kT0aO0hgN_csc-yJ53sAzy_30-zbx8_fL24zK8-f5pfzK5yVZYFz0vVKaWVpriimOGya5lOn6J0DbKBQgLrWs4xp1RWsiK0o5ypptGKdVKBLIvT7N3eux7bAbRK9XrZi7U3g_Q74aQR_0asWYml24iGMV5WPAle3wu8uxkhRDGYoKDvpQU3BkFZTVmBa8wS-uoBeu1Gb9PzEsVTx4qG1Il6s6eUdyF46A7FECymERBpBMTdCCT25d_VH8g_PU_A-R7Ymh52_zeJy_eLvfI3oQfEIA</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Mikulan, Ezequiel</creator><creator>Russo, Simone</creator><creator>Zauli, Flavia Maria</creator><creator>d'Orio, Piergiorgio</creator><creator>Parmigiani, Sara</creator><creator>Favaro, Jacopo</creator><creator>Knight, William</creator><creator>Squarza, Silvia</creator><creator>Perri, Pierluigi</creator><creator>Cardinale, Francesco</creator><creator>Avanzini, Pietro</creator><creator>Pigorini, Andrea</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7259-6120</orcidid></search><sort><creationdate>20211201</creationdate><title>A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation</title><author>Mikulan, Ezequiel ; Russo, Simone ; Zauli, Flavia Maria ; d'Orio, Piergiorgio ; Parmigiani, Sara ; Favaro, Jacopo ; Knight, William ; Squarza, Silvia ; Perri, Pierluigi ; Cardinale, Francesco ; Avanzini, Pietro ; Pigorini, Andrea</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4439-4cfccdcd20620504fb5d097cd7ea8e3ae5fb990922a6a612f295c88dc5facea43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Batch processing</topic><topic>Brain research</topic><topic>Comparative studies</topic><topic>Confidentiality</topic><topic>data sharing</topic><topic>General Data Protection Regulation</topic><topic>geometrical preservation</topic><topic>Identification</topic><topic>Information sharing</topic><topic>Interoperability</topic><topic>Legislation</topic><topic>Localization</topic><topic>Magnetic resonance imaging</topic><topic>Methods</topic><topic>MRI deidentification</topic><topic>Neurosciences</topic><topic>Performance evaluation</topic><topic>Preservation</topic><topic>Privacy</topic><topic>Quality assurance</topic><topic>Risk management</topic><topic>Risk reduction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mikulan, Ezequiel</creatorcontrib><creatorcontrib>Russo, Simone</creatorcontrib><creatorcontrib>Zauli, Flavia Maria</creatorcontrib><creatorcontrib>d'Orio, Piergiorgio</creatorcontrib><creatorcontrib>Parmigiani, Sara</creatorcontrib><creatorcontrib>Favaro, Jacopo</creatorcontrib><creatorcontrib>Knight, William</creatorcontrib><creatorcontrib>Squarza, Silvia</creatorcontrib><creatorcontrib>Perri, Pierluigi</creatorcontrib><creatorcontrib>Cardinale, Francesco</creatorcontrib><creatorcontrib>Avanzini, Pietro</creatorcontrib><creatorcontrib>Pigorini, Andrea</creatorcontrib><collection>Wiley Online Library</collection><collection>Wiley Online Library</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mikulan, Ezequiel</au><au>Russo, Simone</au><au>Zauli, Flavia Maria</au><au>d'Orio, Piergiorgio</au><au>Parmigiani, Sara</au><au>Favaro, Jacopo</au><au>Knight, William</au><au>Squarza, Silvia</au><au>Perri, Pierluigi</au><au>Cardinale, Francesco</au><au>Avanzini, Pietro</au><au>Pigorini, Andrea</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum Brain Mapp</addtitle><date>2021-12-01</date><risdate>2021</risdate><volume>42</volume><issue>17</issue><spage>5523</spage><epage>5534</epage><pages>5523-5534</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><notes>Funding information</notes><notes>European Commission, Grant/Award Number: Horizon 2020 Framework Programme for Research; Horizon 2020 Framework Programme, Grant/Award Numbers: 785907, 945539</notes><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><notes>Funding information European Commission, Grant/Award Number: Horizon 2020 Framework Programme for Research; Horizon 2020 Framework Programme, Grant/Award Numbers: 785907, 945539</notes><abstract>Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re‐identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user‐friendly 3D Slicer plugin‐in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state‐of‐the‐art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re‐identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user‐friendly interface, multiple input–output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.
In this article we present a novel MRI de‐identification method and perform a comparison of its performance with respect to other two state‐of‐the‐art methods. We show that our method performs similarly in terms of de‐identification but better preserves the geometrical properties of the images. It is open‐source and also includes an easy to use graphical user interface.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>34520074</pmid><doi>10.1002/hbm.25639</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-7259-6120</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Batch processing Brain research Comparative studies Confidentiality data sharing General Data Protection Regulation geometrical preservation Identification Information sharing Interoperability Legislation Localization Magnetic resonance imaging Methods MRI deidentification Neurosciences Performance evaluation Preservation Privacy Quality assurance Risk management Risk reduction |
| title | A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation |
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