The first step for neuroimaging data analysis: DICOM to NIfTI conversion
Open accessAbstract
Background Clinical imaging data are typically stored and transferred in the DICOM format, whereas the NIfTI format has been widely adopted by scientists in the neuroimaging community. Therefore, a vital initial step in processing the data is to convert images from the complicated DICOM format to the much simpler NIfTI format. While there are a number of tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt this process.New method We provide some insight into the challenges faced with image conversion. First, different manufacturers implement the DICOM format differently which complicates the conversion. Second, different modalities and sub-modalities may need special treatment during conversion. Lastly, the image transferring and archiving can also impact the DICOM conversion.Results We present results in several error-prone domains, including the slice order for functional imaging, phase encoding direction for distortion correction, effect of diffusion gradient direction, and effect of gantry correction for some imaging modality.Comparison with existing methods Conversion tools are often designed for a specific manufacturer or modality. The tools and insight we present here are aimed at different manufacturers or modalities.Conclusions The imaging conversion is complicated by the variation of images. An understanding of the conversion basics can be helpful for identifying the source of the error. Here we provide users with simple methods for detecting and correcting problems. This also serves as an overview for developers who wish to either develop their own tools or adapt the open source tools created by the authors.
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Source data files were converted to NifTI format using dcm2niix and organized and named following the Brain Imaging Data Structure (BIDS).
“To provide a single standard format, the sources data files were converted to NifTI format files using the dcm2niix 54 conversion program and are organized and named following the Brain Imaging Data Structure 55 (BIDS).”
From OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019) · cites this paper as [54]
Converting DICOM to NIfTI files eliminates all patient-identifiable metadata from the header portion of the DICOM format.
“The conversion of DICOM to NIfTI files has the benefit of eliminating all patient-identifiable metadata from the header portion of the DICOM format80,81.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [80]
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- Authors
- Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C.
- Affiliations
- The Ohio State University (US)
- Nottingham University Hospitals NHS Trust (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- University of Oregon (US)
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 264
- Pages
- 47-56
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2290321704
- Cited by
- 1,073
- References
- 16
- Citations by year
- 2026: 174 · 2025: 189 · 2024: 167 · 2023: 123 · 2022: 138 · 2021: 111 · 2020: 88 · 2019: 45 · 2018: 26 · 2017: 9 · 2016: 2 · 2013: 1
- FWCI
- 23.71
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Licence
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust (091593)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- neuroimaging data processing, image format conversion, phase-encoding direction, distortion correction, image archiving
- MeSH terms
- Humans, Image Processing, Computer-Assisted, Neuroimaging, Functional Neuroimaging
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