A comparative study between state‐of‐the‐art MRI deidentification and AnonyMI, a new method combining re‐identification risk reduction and geometrical preservation. Issue 17 (14th September 2021) - 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
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. Abstract : 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.