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Medical Imaging Data Formats

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Magnetic Resonance Brain Imaging

Part of the book series: Use R! ((USE R))

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Abstract

There exists a large variety of data formats used in medical imaging in general and specifically for functional Magnetic Resonance Imaging, diffusion-weighted imaging, Multi-Parameter Map**, or inversion recovery Magnetic Resonance Imaging. Medical imaging data typically contain the actual data and additionally some metadata. This may be the data dimensionality, the spatial extension of the imaged voxel, but also physical parameters of the image acquisition, or patient data. The way this is stored in the different data formats differs. Here, we discuss DICOM), ANALYZE, and NIfTI formats as they are widely used for storing medical imaging data or analysis results that are interchangeable between different analysis software. We demonstrate how these data can be easily accessed from within R. This is amended with a short discussion of the Brain Imaging Data Structure (BIDS) standard.

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Notes

  1. 1.

    DICOM is the registered trademark of the National Electrical Manufacturers Association for its standards publications relating to digital communications of medical information.

  2. 2.

    We use function rimage from adimpro , which is a wrapper to image to avoid excessive parameter specifications.

  3. 3.

    If appropriate! There are, e.g., entries in the metadata section, that refer to the data range, like dscal_min, these are set accordingly.

References

  • Chambers, J.M.: Software for Data Analysis: Programming with R. Statistics and Computing. Springer, Berlin (2008). https://doi.org/10.1007/978-0-387-75936-4

  • Clayden, J.: tractor.base: a package for reading, manipulating and visualising magnetic resonance images (2020). R package version 3.3.3.1

    Google Scholar 

  • Cox, R.W.: AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996). https://doi.org/10.1006/cbmr.1996.0014

    Article  Google Scholar 

  • Gorgolewski, K.J., Auer, T., Calhoun, V.D., Craddock, R.C., Das, S., Duff, E.P., Flandin, G., Ghosh, S.S., Glatard, T., Halchenko, Y.O., Handwerker, D.A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A.: The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3, 160044 (2016). https://doi.org/10.1038/sdata.2016.44

  • Hanke, M., Baumgartner, F.J., Ibe, P., Kaule, F.R., Pollmann, S., Speck, O., Zinke, W., Stadler, J.: A high-resolution 7-tesla fMRI dataset from complex natural stimulation with an audio movie. Sci Data 1, 140003 (2014). https://doi.org/10.1038/sdata.2014.3

    Article  Google Scholar 

  • Ioannidis, J.P.A.: Why most published research findings are false. PLoS Med 2(8), e124 (2005). https://doi.org/10.1371/journal.pmed.0020124

    Article  Google Scholar 

  • National Electrical Manufacturers Association: DICOM digital imaging and communications in medicine (2019). https://www.dicomstandard.org/

  • NeuroDebian: nifti2dicom—convert 3D medical images to DICOM 2D series (2019). http://neuro.debian.net/pkgs/nifti2dicom.html

  • Nichols, T.E., Das, S., Eickhoff, S.B., Evans, A.C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M.P., Poldrack, R.A., Poline, J.-B., Proal, E., Thirion, B., Van Essen, D.C., White, T., Yeo, B.T.T.: Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci. 20(3), 299–303 (2017). https://doi.org/10.1038/nn.4500

    Article  Google Scholar 

  • Ooms, J.: The jsonlite package: A practical and consistent map** between JSON data and R objects (2014). ar**%20between%20JSON%20data%20and%20R%20objects%20%282014%29.%20ar**v%3A1403.2805%20%5Bstat.CO%5D"> Google Scholar 

  • Poldrack, R.A., Gorgolewski, K.J.: OpenfMRI: open sharing of task fMRI data. Neuroimage 144(Pt B), 259–261 (2017). https://doi.org/10.1016/j.neuroimage.2015.05.073

  • Poldrack, R.A., Laumann, T.O., Koyejo, O., Gregory, B., Hover, A., Chen, M.-Y., Gorgolewski, K.J., Luci, J., Joo, S.J., Boyd, R.L., Hunicke-Smith, S., Simpson, Z.B., Caven, T., Sochat, V., Shine, J.M., Gordon, E., Snyder, A.Z., Adeyemo, B., Petersen, S.E., Glahn, D.C., Reese Mckay, D., Curran, J.E., Göring, H.H.H., Carless, M.A., Blangero, J., Dougherty, R., Leemans, A., Handwerker, D.A., Frick, L., Marcotte, E.M., Mumford, J.A.: Long-term neural and physiological phenoty** of a single human. Nat. Commun. 6, 8885 (2015). https://doi.org/10.1038/ncomms9885

  • Stanford Center For Reproducible Neuroscience: BIDS tutorial series: Automate the introductory walkthrough (2019a). http://reproducibility.stanford.edu/bids-tutorial-series-part-1b/

  • Stanford Center For Reproducible Neuroscience: BIDS tutorial series: Introductory walkthrough (2019b). http://reproducibility.stanford.edu/bids-tutorial-series-part-1a/

  • Stanford Center for Reproducible Neuroscience: OpenNeuro A free and open platform for analyzing and sharing neuroimaging data (2019). https://openneuro.org/

  • Tabelow, K., Polzehl, J.: fmri: Analysis of fMRI Experiments (2023). R package version 1.9.10

    Google Scholar 

  • The FIL Methods Group (and honorary members): SPM12 Manual. Functional Imaging Laboratory, Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London (2021)

    Google Scholar 

  • Whitcher, B.: oro.dicom: Rigorous—DICOM Input/Output (2019). R package version 0.5.3

    Google Scholar 

  • Whitcher, B., Schmid, V., Thornton, A.: oro.nifti: Rigorous—NIfTI Input/Output (2022). R package version 0.11.4

    Google Scholar 

  • Whitcher, B., Schmid, V.J., Thornton, A.: Working with the DICOM and NIfTI data standards in R. J. Statist. Softw. 44(6), 1–28 (2011). https://doi.org/10.18637/jss.v044.i06

    Article  Google Scholar 

  • Wickham, H.: Advanced R, 2nd edn. Chapmab & Hall/CRC The R Series. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  • WU-Minn HCP Consortium: ConnectomeDB–sharing human brain connectivity data (2019a). https://db.humanconnectome.org/app/template/Login.vm

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Authors and Affiliations

  1. Berlin, Germany

    Jörg Polzehl

  2. WIAS Berlin, Berlin, Germany

    Karsten Tabelow

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  1. Jörg Polzehl
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  2. Karsten Tabelow
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Correspondence to Karsten Tabelow .

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Polzehl, J., Tabelow, K. (2023). Medical Imaging Data Formats. In: Magnetic Resonance Brain Imaging. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-031-38949-8_3

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