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.

A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation / E. Mikulan, S. Russo, F.M. Zauli, P. d'Orio, S. Parmigiani, J. Favaro, W. Knight, S. Squarza, P. Perri, F. Cardinale, P. Avanzini, A. Pigorini. - In: HUMAN BRAIN MAPPING. - ISSN 1065-9471. - (2021), pp. 1-12. [Epub ahead of print] [10.1002/hbm.25639]

A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation

E. Mikulan
Primo
;
S. Russo
Secondo
;
F.M. Zauli;P. d'Orio;S. Parmigiani;S. Squarza;P. Perri;A. Pigorini
Ultimo
2021

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.
data sharing; geometrical preservation; MRI deidentification; privacy
Settore BIO/09 - Fisiologia
H20_RIA18MMASS_01 - Human Brain Project Specific Grant Agreement 2 (HBP SGA2) - MASSIMINI, MARCELLO - H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action - 2018
Human Brain Project Specific Grant Agreement 3 (HBP SGA3)
14-set-2021
Article (author)
File in questo prodotto:
File Dimensione Formato  
hbm.25639.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 2.61 MB
Formato Adobe PDF
2.61 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/869826
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact