Souheil J Inati1, Joseph D Naegele1, Nicholas R Zwart2, Vinai Roopchansingh1, Martin J Lizak3, David C Hansen4, Chia-Ying Liu5, David Atkinson6, Peter Kellman7, Sebastian Kozerke8, Hui Xue7, Adrienne E Campbell-Washburn7, Thomas S Sørensen9, Michael S Hansen7. 1. National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA. 2. Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA. 3. National Institute of Neurologic Disease and Stroke, National Institutes of Health, Bethesda, Maryland, USA. 4. Department of Oncology, Aarhus University Hospital, Aarhus, Denmark. 5. Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA. 6. Centre for Medical Image Computing, University College, London, UK. 7. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA. 8. Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland. 9. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Abstract
PURPOSE: This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing. METHODS: A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using magnetic resonance imaging scanners from four vendors, converted to ISMRM Raw Data format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). RESULTS: Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. CONCLUSION: The proposed raw data format solves a practical problem for the magnetic resonance imaging community. It may serve as a foundation for reproducible research and collaborations. The ISMRM Raw Data format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. Magn Reson Med 77:411-421, 2017.
PURPOSE: This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing. METHODS: A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using magnetic resonance imaging scanners from four vendors, converted to ISMRM Raw Data format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). RESULTS: Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. CONCLUSION: The proposed raw data format solves a practical problem for the magnetic resonance imaging community. It may serve as a foundation for reproducible research and collaborations. The ISMRM Raw Data format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. Magn Reson Med 77:411-421, 2017.
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