qMRI-BIDS: an extension to the brain imaging data structure for quantitative magnetic resonance imaging data

[en] The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others
Neurology

Author, co-author :

Karakuzu, Agah

Appelhoff, Stefan

Auer, Tibor

Boudreau, Mathieu

Feingold, Franklin

Khan, Ali

Lazari, Alberto

Markiewicz, Chris

Mulder, Martijn

Phillips, Christophe ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing

More authors (4 more)

Language :

English

Title :

qMRI-BIDS: an extension to the brain imaging data structure for quantitative magnetic resonance imaging data

Publication date :

24 August 2022

Journal title :

Scientific Data

eISSN :

2052-4463

Publisher :

Springer Science and Business Media LLC

Volume :

Issue :

517

Pages :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.nature.com/articles/s41597-022-01571-4.pdf

Funders :

Canada First Research Excellence Fund
International Society for Magnetic Resonance in Medicine
Canadian Open Neuroscience Platform
RCUK | Biotechnology and Biological Sciences Research Council
Wellcome Trust [GB]
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Rubicon grant by Dutch Research Council

Available on ORBi :

since 29 August 2022

Statistics

Number of views

67 (6 by ULiège)

Number of downloads

53 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

Bibliography

Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data 3, 1–9, 10.1038/sdata.2016.44 (2016). DOI: 10.1038/sdata.2016.44
Markiewicz, C. J. et al. OpenNeuro: An open resource for sharing of neuroimaging data. bioRxiv, 2021.2006.2028.450168, https://doi.org/10.1101/2021.06.28.450168 (2021).
Poldrack, R. et al. Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in Neuroinformatics 7, 12 (2013). DOI: 10.3389/fninf.2013.00012
Gorgolewski, K. J. et al. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS computational biology 13, e1005209 (2017). DOI: 10.1371/journal.pcbi.1005209
Esteban, O. et al. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PloS one 12, e0184661 (2017). DOI: 10.1371/journal.pone.0184661
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods 16, 111–116 (2019). DOI: 10.1038/s41592-018-0235-4
Inati, S. J. et al. ISMRM Raw data format: A proposed standard for MRI raw datasets. Magnetic resonance in medicine 77, 411–421, 10.1002/mrm.26089 (2017). DOI: 10.1002/mrm.26089
Hansen, M. S. & Sørensen, T. S. Gadgetron: an open source framework for medical image reconstruction. Magnetic resonance in medicine 69, 1768–1776, 10.1002/mrm.24389 (2013). DOI: 10.1002/mrm.24389
Maier, O. et al. CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge. Magnetic resonance in medicine 85, 1821–1839, 10.1002/mrm.28569 (2021). DOI: 10.1002/mrm.28569
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data 3, 1–9 (2016). DOI: 10.1038/sdata.2016.18
Novikov, D. S., Kiselev, V. G. & Jespersen, S. N. On modeling. Magn Reson Med 79, 3172–3193, 10.1002/mrm.27101 (2018). DOI: 10.1002/mrm.27101
Gupta, R. K. A new look at the method of variable nutation angle for the measurement of spin-lattice relaxation times using fourier transform NMR. Journal of Magnetic Resonance 25, 231–235, 10.1016/0022-2364(77)90138-X (1977). DOI: 10.1016/0022-2364(77)90138-X
Stikov, N., Trzasko, J. D. & Bernstein, M. A. Reproducibility and the future of MRI research. Magnetic Resonance in Medicine 82, 1981–1983, 10.1002/mrm.27939 (2019). DOI: 10.1002/mrm.27939
Lundervold, A. S. & Lundervold, A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik 29, 102–127 (2019). DOI: 10.1016/j.zemedi.2018.11.002
Golkov, V. et al. Q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE transactions on medical imaging 35, 1344–1351 (2016). DOI: 10.1109/TMI.2016.2551324
Yoo, Y. et al. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage: Clinical 17, 169–178 (2018). DOI: 10.1016/j.nicl.2017.10.015
Lyu, Q. & Wang, G. Quantitative MRI: absolute T1, T2 and proton density parameters from deep learning. arXiv preprint arXiv:1806.07453 (2018).
Wu, Y., Ma, Y., Du, J. & Xing, L. Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning. Magnetic Resonance Imaging 72, 78–86 (2020). DOI: 10.1016/j.mri.2020.06.011
Bojorquez, J. Z. et al. What are normal relaxation times of tissues at 3 T. Magn Reson Imaging 35, 69–80, 10.1016/j.mri.2016.08.021 (2017). DOI: 10.1016/j.mri.2016.08.021
Weinberger, D. R. & Radulescu, E. Finding the Elusive Psychiatric “Lesion” With 21st-Century Neuroanatomy: A Note of Caution. American Journal of Psychiatry 173, 27–33, 10.1176/appi.ajp.2015.15060753 (2015). DOI: 10.1176/appi.ajp.2015.15060753
Lorio, S. et al. Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. Human brain mapping 37, 1801–1815 (2016). DOI: 10.1002/hbm.23137
Draganski, B. et al. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55, 1423–1434 (2011). DOI: 10.1016/j.neuroimage.2011.01.052
Lommers, E. et al. Voxel-Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis. Human Brain Mapping 42, 1003–1012, 10.1002/hbm.25274 (2021). DOI: 10.1002/hbm.25274
Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Current opinion in neurology 28, 313–322 (2015). DOI: 10.1097/WCO.0000000000000222
Weiskopf, N., Callaghan, M. F., Josephs, O., Lutti, A. & Mohammadi, S. Estimating the apparent transverse relaxation time (R2*) from images with different contrasts (ESTATICS) reduces motion artifacts. Frontiers in neuroscience 8, 278 (2014). DOI: 10.3389/fnins.2014.00278
Lutti, A., Dick, F., Sereno, M. I. & Weiskopf, N. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage 93, 176–188 (2014). DOI: 10.1016/j.neuroimage.2013.06.005
Haast, R. A. M., Ivanov, D. & Formisano, E. & Uludaǧ, K. Reproducibility and reliability of quantitative and weighted T1 and T2∗ mapping for myelin-based cortical parcellation at 7 Tesla. Frontiers in neuroanatomy 10, 112 (2016). DOI: 10.3389/fnana.2016.00112
Dinse, J. et al. A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI. Neuroimage 114, 71–87 (2015). DOI: 10.1016/j.neuroimage.2015.04.023
Mancini, M. et al. An interactive meta-analysis of MRI biomarkers of myelin. eLife 9, 10.7554/elife.61523 (2020).
Lazari, A. & Lipp, I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage, 117744, https://doi.org/10.1016/j.neuroimage.2021.117744 (2021).
Caan, M. W. A. et al. MP2RAGEME: T1, T2*, and QSM mapping in one sequence at 7 tesla. Human brain mapping 40, 1786–1798 (2019). DOI: 10.1002/hbm.24490
Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Front Neurosci 7, 95, 10.3389/fnins.2013.00095 (2013). DOI: 10.3389/fnins.2013.00095
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of neuroscience methods 264, 47–56 (2016). DOI: 10.1016/j.jneumeth.2016.03.001
Yarnykh, V. L. Optimal radiofrequency and gradient spoiling for improved accuracy of T1 and B1 measurements using fast steady-state techniques. Magnetic Resonance in Medicine 63, 1610–1626, 10.1002/mrm.22394 (2010). DOI: 10.1002/mrm.22394
Layton, K. J. et al. Pulseq: a rapid and hardware‐independent pulse sequence prototyping framework. Magnetic resonance in medicine 77, 1544–1552, 10.1002/mrm.26235 (2017). DOI: 10.1002/mrm.26235
Ravi, K. S., Geethanath, S. & Vaughan, J. T. PyPulseq: A python package for mri pulse sequence design. Journal of Open Source Software 4, 1725 (2019). DOI: 10.21105/joss.01725
Cordes, C., Konstandin, S., Porter, D. & Günther, M. Portable and platform‐independent MR pulse sequence programs. Magnetic resonance in medicine 83, 1277–1290, 10.1002/mrm.28020 (2020). DOI: 10.1002/mrm.28020
Nielsen, J. F. & Noll, D. C. TOPPE: A framework for rapid prototyping of MR pulse sequences. Magnetic resonance in medicine 79, 3128–3134, 10.1002/mrm.26990 (2018). DOI: 10.1002/mrm.26990
Magland, J. F., Li, C., Langham, M. C. & Wehrli, F. W. Pulse sequence programming in a dynamic visual environment: SequenceTree. Magnetic resonance in medicine 75, 257–265, 10.1002/mrm.25640 (2016). DOI: 10.1002/mrm.25640
Jochimsen, T. H. & Von Mengershausen, M. ODIN—object-oriented development interface for NMR. Journal of Magnetic Resonance 170, 67–78, 10.1016/j.jmr.2004.05.021 (2004). DOI: 10.1016/j.jmr.2004.05.021
Santos, J. M., Wright, G. A. & Pauly, J. M. In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1048–1051 (2004).
Karakuzu, A., Biswas, L., Cohen-Adad, J. & Stikov, N. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magnetic Resonance in Medicine 88, 1212–1228, 10.1002/mrm.29292 (2022). DOI: 10.1002/mrm.29292
Tabelow, K. et al. hMRI–A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191–210 (2019). DOI: 10.1016/j.neuroimage.2019.01.029
Karakuzu, A. et al. qMRLab: Quantitative MRI analysis, under one umbrella. Journal of Open Source Software 5, 2343, 10.21105/joss.02343 (2020). DOI: 10.21105/joss.02343
Wood, T. C. QUIT: QUantitative imaging tools. Journal of Open Source Software 3, 656 (2018). DOI: 10.21105/joss.00656
Maier, O., Spann, S. M., Bödenler, M. & Stollberger, R. PyQMRI: an accelerated Python based quantitative MRI toolbox. Journal of Open Source Software 5, 2727 (2020). DOI: 10.21105/joss.02727
Froeling, M. QMRTools: a Mathematica toolbox for quantitative MRI analysis. Journal of Open Source Software 4, 1204 (2019). DOI: 10.21105/joss.01204
Mezer, A. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nature medicine 19, 1667–1672 (2013). DOI: 10.1038/nm.3390
Berks, M. m Parker, G. J., Little, R. & Cheung, S. Madym: A C++ toolkit for quantitative DCE-MRI analysis. Journal of Open Source Software 6, 3523 (2021). DOI: 10.21105/joss.03523
Debus, C. et al. MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging–design, implementation and application on the example of DCE-MRI. BMC bioinformatics 20, 1–18 (2019). DOI: 10.1186/s12859-018-2588-1
Barnes, S. R. et al. ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC medical imaging 15, 1–20 (2015). DOI: 10.1186/s12880-015-0062-3
Smith, D. S., Li, X., Arlinghaus, L. R., Yankeelov, T. E. & Welch, E. B. DCEMRI. jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ 3, e909 (2015). DOI: 10.7717/peerj.909
Ortuño, J. E. et al. DCE@ urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data. BMC bioinformatics 14, 1–17 (2013).
Weiskopf, N., Edwards, L. J., Helms, G., Mohammadi, S. & Kirilina, E. Quantitative magnetic resonance imaging of brain anatomy and in vivo histology. Nature Reviews Physics 3, 570–588, 10.1038/s42254-021-00326-1 (2021). DOI: 10.1038/s42254-021-00326-1
Yarkoni, T. et al. PyBIDS: Python tools for BIDS datasets. Journal of open source software 4 (2019).
Niso, G. et al. Open and reproducible neuroimaging: from study inception to publication. OSF Preprints 10.31219/osf.io/pu5vb (2022).
DuPre, E. et al. Beyond advertising: New infrastructures for publishing integrated research objects. PLOS Computational Biology 18, e1009651, 10.1371/journal.pcbi.1009651 (2022). DOI: 10.1371/journal.pcbi.1009651
Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281, 10.1016/j.neuroimage.2009.10.002 (2010). DOI: 10.1016/j.neuroimage.2009.10.002
Karakuzu, A. et al. qMRI-BIDS example dataset. Open Science Framework 10.17605/osf.io/k4bs5 (2021).
Cohen-Adad, J. et al. Spine Generic Public Database (Multi-Subject). Zenodo 10.5281/zenodo.4299139 (2020).
Bellec, P. & Boyle, J. Bridging the gap between perception and action: the case for neuroimaging, AI and video games. https://doi.org/10.31234/osf.io/3epws (2019).
Karakuzu, A., Cohen-Adad, J. & Stikov, N. Multicenter VENUS dataset: Comparing vendor-neutral and vendor-native MTsat acquisitions. Open Science Framework 10.17605/osf.io/5n3cu (2022).
Callaghan, M. F. et al. Example dataset for the hMRI toolbox. Data in Brief 25, 104132, 10.1016/j.dib.2019.104132 (2019). DOI: 10.1016/j.dib.2019.104132
de Hollander, G. Gilles86/pymp2rage: First alpha version pymp2rage. Zenodo 10.5281/zenodo.1476976 (2018).
Barral, J. K. et al. A robust methodology for in vivo T1 mapping. Magnetic resonance in medicine 64, 1057–1067 (2010). DOI: 10.1002/mrm.22497
Carr, H. Y. & Purcell, E. M. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical review 94, 630 (1954). DOI: 10.1103/PhysRev.94.630
MacKay, A. et al. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med 31, 673–677 (1994). DOI: 10.1002/mrm.1910310614
Ma, J. & Wehrli, F. W. Method for image-based measurement of the reversible and irreversible contribution to the transverse-relaxation rate. Journal of Magnetic Resonance, Series B 111, 61–69 (1996). DOI: 10.1006/jmrb.1996.0060
Wolff, S. D. & Balaban, R. S. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magnetic resonance in medicine 10, 135–144 (1989). DOI: 10.1002/mrm.1910100113
Helms, G., Dathe, H. & Dechent, P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magn Reson Med 59, 667–672, 10.1002/mrm.21542 (2008). DOI: 10.1002/mrm.21542
Insko, E. K. & Bolinger, L. Mapping of the radiofrequency field. Journal of Magnetic Resonance, Series A 103, 82–85 (1993). DOI: 10.1006/jmra.1993.1133
Jiru, F. & Klose, U. Fast 3D radiofrequency field mapping using echo-planar imaging. Magnetic Resonance in Medicine 56, 1375–1379, 10.1002/mrm.21083 (2006). DOI: 10.1002/mrm.21083
Yarnykh, V. L. Actual flip-angle imaging in the pulsed steady state: a method for rapid three-dimensional mapping of the transmitted radiofrequency field. Magn Reson Med 57, 192–200 (2007). DOI: 10.1002/mrm.21120
Chung, S., Kim, D., Breton, E. & Axel, L. Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout. Magnetic resonance in medicine 64, 439–446 (2010). DOI: 10.1002/mrm.22423
Eggenschwiler, F., Kober, T., Magill, A. W., Gruetter, R. & Marques, J. P. SA2RAGE: A new sequence for fast B1+‐mapping. Magnetic resonance in medicine 67, 1609–1619 (2012). DOI: 10.1002/mrm.23145
Papp, D., Callaghan, M. F., Meyer, H., Buckley, C. & Weiskopf, N. Correction of inter‐scan motion artifacts in quantitative R1 mapping by accounting for receive coil sensitivity effects. Magnetic resonance in medicine 76, 1478–1485 (2016). DOI: 10.1002/mrm.26058