| Title: |
HeuDiConv — flexible DICOM conversion into structured directory layouts |
| Authors: |
Halchenko, Yaroslav O.; Goncalves, Mathias; Ioanas, Horea-Ioan; Rorden, Chris; Hendrickson, Timothy J.; Dayan, Michael; Houlihan, Sean Dae; Kent, James; Strauss, Ted; Lee, John; To, Isaac; Markiewicz, Christopher J.; Ghosh, Satrajit; Lukas, Darren; Butler, Ellyn R.; Thompson, Todd; Termenon, Maite; Smith, David V.; Macdonald, Austin; Kennedy, David N.; Velasco, Pablo; Visconti di Oleggio Castello, Matteo; Salo, Taylor; Wodder, John T.; Hanke, Michael; Sadil, Patrick; Gorgolewski, Krzysztof Jacek |
| Source: |
The journal of open source software 9(99), 5839 - (2024). doi:10.21105/joss.05839 |
| Publisher Information: |
[Verlag nicht ermittelbar] |
| Publication Year: |
2024 |
| Collection: |
Forschungszentrum Jülich: JuSER (Juelich Shared Electronic Resources) |
| Subject Terms: |
info:eu-repo/classification/ddc/004 |
| Subject Geographic: |
DE |
| Description: |
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 ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
2475-9066 |
| Relation: |
info:eu-repo/semantics/altIdentifier/issn/2475-9066 |
| Availability: |
https://juser.fz-juelich.de/record/1031830; https://juser.fz-juelich.de/search?p=id:%22FZJ-2024-05845%22 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.58573E63 |
| Database: |
BASE |