Osprey is an all-in-one software suite for state-of-the art processing and quantitative analysis of in-vivo magnetic resonance spectroscopy (MRS) data.
Contact, Feedback, Suggestions
We also welcome your direct contributions to Osprey in the Osprey GitHub repository.
Should you publish material that made use of Osprey, please cite the following publication:
G Oeltzschner, HJ Zöllner, SCN Hui, M Mikkelsen, MG Saleh, S Tapper, RAE Edden. Osprey: Open-Source Processing, Reconstruction & Estimation of Magnetic Resonance Spectroscopy Data. J Neurosci Meth 343:108827 (2020).
This work has been supported by NIH grants R01EB016089, P41EB15909, R01EB023963, and K99AG062230.
We wish to thank the following individuals for their contributions to the development of Osprey and shared processing code:
- Jamie Near (McGill University, Montreal)
- Ralph Noeske (GE Healthcare, Berlin)
- Peter Barker (Johns Hopkins University, Baltimore)
- Robin de Graaf (Yale School of Medicine, New Haven)
- Philipp Ehses (German Center for Neurodegenerative Diseases, Bonn)
- Wouter Potters (UMC Amsterdam)
- Xiangrui Li (Ohio State University, Columbus)
We are particularly grateful for the incredible raincloud plot tools developed by Micah Allen, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, and Rogier Kievit. Should you make use of the OspreyOverview raincloud plots, please consider citing their original publications:
- Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization [version 1; peer review: 2 approved]. Wellcome Open Res 2019, 4:63. DOI: 10.12688/wellcomeopenres.15191.1
- Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit R. (2018) RainCloudPlots tutorials and codebase (Version v1.1). Zenodo. http://doi.org/10.5281/zenodo.3368186