In clinical and pre-clinical research involving medical images, the first step following a Magnetic Resonance Imaging dataset acquisition, usually entails the conversion of image data from the native scanner format to a format suitable for the intended analysis. The proprietary Bruker ParaVision software currently does not provide the tools for conversion of the data to suitable and open formats for research, such as nifti (Cox, Robert W and Ashburner, John and Breman, Hester and Fissell, Kate and Haselgrove, Christian and Holmes, Colin J and Lancaster, Jack L and Rex, David E and Smith, Stephen M and Woodward, Jeffrey B and others 2004), for which most of the available tools for medical image analysis are implemented.
For this purpose we have designed and developed bruker2nifti, a pip-installable Python tool provided with a Graphical User Interface to convert from the native MRI Bruker format to the nifti format, without any intermediate step through the DICOM standard formats (Mildenberger, Eichelberg, and Martin 2002).
Bruker2nifti is intended to be a tool to access the data structure and to parse all parameter files of the Bruker ParaVision format into python dictionaries, to select the relevant information to fill the Nifti header and data volume. Lastly it is meant to be a starting point where to integrate possible future variations in Bruker hardware and ParaVision software future releases.
This work was supported by Wellcome / Engineering and Physical Sciences Research Council (EPSRC) [WT101957; NS/A000027/1; 203145Z/16/Z]. Sebastiano Ferraris is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1) and Doctoral Training Grant (EP/M506448/1). Hannes Van Der Merwe is co-funded with support of the Erasmus + Programme of the European Union (Framework Agreement number: 2013-0040). This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein. We would also like to thank all the people who directly contributed to bruker2nifti with offline hints and suggestions: Bernard Siow (Centre for Advanced Biomedical Imaging, University College London), Chris Rorden (McCausland Center for Brain Imaging, University of South Carolina) and Matthew Brett (Berkeley Brain Imaging Center).
Cox, Robert W and Ashburner, John and Breman, Hester and Fissell, Kate and Haselgrove, Christian and Holmes, Colin J and Lancaster, Jack L and Rex, David E and Smith, Stephen M and Woodward, Jeffrey B and others. 2004. “A (Sort of) New Image Data Format Standard: Nifti-1.” Neuroimage 22: e1440.
Mildenberger, Peter, Marco Eichelberg, and Eric Martin. 2002. “Introduction to the Dicom Standard.” European Radiology 12 (4): 920–27. doi:10.1007/s003300101100.