The medical imaging use case (Use Case 2), led by Stichting Radboud Universiteit, aims to collect and curate a set of large datasets representative of medical (neuro)imaging applications.
Building on this foundation, the use case focuses on establishing strategies and techniques for staging (neuro)imaging data in combination with tabular (meta)data related to individual participants, including demographic, health, and questionnaire data, within the SIESTA storage platform.
In addition, it seeks to develop strategies to enable the use of existing medical (neuro)imaging analysis pipelines (such as FSL, SPM, FieldTrip and EEGLAB) on the SIESTA compute platform, including scenarios where the underlying data cannot be publicly shared. Particular attention is given to the implementation of methods for deriving, staging and sharing non-identifiable group-level analysis results.
The use case also addresses the development of quantitative methods to assess the risk of identifiability associated with datasets, analysis techniques and data masking approaches. Finally, it includes the design of a user-friendly interface that facilitates access to these analysis pipelines.
In this context, specific tools such as BidScramble for BIDS data anonymisation, MetaprivBIDS for privacy risk assessment, and DatLeak for analysing potential information leakage in neuroimaging and tabular datasets support the implementation of these objectives.