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@ARTICLE{Halchenko:1031830,
      author       = {Halchenko, Yaroslav O. and Goncalves, Mathias and Ghosh,
                      Satrajit and Velasco, Pablo and Visconti di Oleggio
                      Castello, Matteo and Salo, Taylor and Wodder, John T. and
                      Hanke, Michael and Sadil, Patrick and Gorgolewski, Krzysztof
                      Jacek and Ioanas, Horea-Ioan and Rorden, Chris and
                      Hendrickson, Timothy J. and Dayan, Michael and Houlihan,
                      Sean Dae and Kent, James and Strauss, Ted and Lee, John and
                      To, Isaac and Markiewicz, Christopher J. and Lukas, Darren
                      and Butler, Ellyn R. and Thompson, Todd and Termenon, Maite
                      and Smith, David V. and Macdonald, Austin and Kennedy, David
                      N.},
      title        = {{H}eu{D}i{C}onv — flexible {DICOM} conversion into
                      structured directory layouts},
      journal      = {The journal of open source software},
      volume       = {9},
      number       = {99},
      issn         = {2475-9066},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {[Verlag nicht ermittelbar]},
      reportid     = {FZJ-2024-05845},
      pages        = {5839 -},
      year         = {2024},
      note         = {Open Source Initiative8605 Santa Monica Blvd PMB 63639West
                      Hollywood, CA 90069-4109United StatesThe Open Source
                      Initiative’s IRS Tax ID Number (TIN) is 91-2037395.The
                      Open Source Initiative’s EU Transparency Register Number
                      672028337929-77},
      abstract     = {In order to support efficient processing, data must be
                      formatted according to standards thatare prevalent in the
                      field and widely supported among actively developed analysis
                      tools. TheBrain Imaging Data Structure (BIDS) (Gorgolewski
                      et al., 2016) is an open standard designedfor computational
                      accessibility, operator legibility, and a wide and easily
                      extendable scopeof modalities — and is consequently used
                      by numerous analysis and processing tools as thepreferred
                      input format in many fields of neuroscience. HeuDiConv
                      (Heuristic DICOM Converter)enables flexible and efficient
                      conversion of spatially reconstructed neuroimaging data
                      fromthe DICOM format (quasi-ubiquitous in biomedical image
                      acquisition systems, particularlyin clinical settings) to
                      BIDS, as well as other file layouts. HeuDiConv provides a
                      multi-stageoperator input workflow (discovery, manual
                      tuning, conversion) where a manual tuning step isoptional
                      and the entire conversion can thus be seamlessly integrated
                      into a data processingpipeline. HeuDiConv is written in
                      Python, and supports the DICOM specification for input
                      parsing, and the BIDS specification for output construction.
                      The support for these standardsis extensive, and HeuDiConv
                      can handle complex organization scenarios that arise for
                      specificdata types (e.g., multi-echo sequences, or
                      single-band reference volumes). In addition togenerating
                      valid BIDS outputs, additional support is offered for custom
                      output layouts. Thisis obtained via a set of built-in fully
                      functional or example heuristics expressed as simplePython
                      functions. Those heuristics could be taken as a template or
                      as a base for developingcustom heuristics, thus providing
                      full flexibility and maintaining user accessibility.
                      HeuDiConvfurther integrates with DataLad (Halchenko et al.,
                      2021), and can automatically preparehierarchies of DataLad
                      datasets with optional obfuscation of sensitive data and
                      metadata,including obfuscating patient visit timestamps in
                      the git version control system. As a result,given its
                      extensibility, large modality support, and integration with
                      advanced data managementtechnologies, HeuDiConv has become a
                      mainstay in numerous neuroimaging workflows, andconstitutes
                      a powerful and highly adaptable tool of potential interest
                      to large swathes of theneuroimaging community.},
      cin          = {INM-7},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21105/joss.05839},
      url          = {https://juser.fz-juelich.de/record/1031830},
}