Title | Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Oeltzschner G, Zöllner HJ, Hui SCN, Mikkelsen M, Saleh MG, Tapper S, Edden RAE |
Journal | J Neurosci Methods |
Volume | 343 |
Pagination | 108827 |
Date Published | 2020 Sep 01 |
ISSN | 1872-678X |
Keywords | Ecosystem, Healthy Volunteers, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Software |
Abstract | BACKGROUND: Processing and quantitative analysis of magnetic resonance spectroscopy (MRS) data are far from standardized and require interfacing with third-party software. Here, we present Osprey, a fully integrated open-source data analysis pipeline for MRS data, with seamless integration of pre-processing, linear-combination modelling, quantification, and data visualization. NEW METHOD: Osprey loads multiple common MRS data formats, performs phased-array coil combination, frequency-and phase-correction of individual transients, signal averaging and Fourier transformation. Linear combination modelling of the processed spectrum is carried out using simulated basis sets and a spline baseline. The MRS voxel is coregistered to an anatomical image, which is segmented for tissue correction and quantification is performed based upon modelling parameters and tissue segmentation. The results of each analysis step are visualized in the Osprey GUI. The analysis pipeline is demonstrated in 12 PRESS, 11 MEGA-PRESS, and 8 HERMES datasets acquired in healthy subjects. RESULTS: Osprey successfully loads, processes, models, and quantifies MRS data acquired with a variety of conventional and spectral editing techniques. COMPARISON WITH EXISTING METHOD(S): Osprey is the first MRS software to combine uniform pre-processing, linear-combination modelling, tissue correction and quantification into a coherent ecosystem. Compared to existing compiled, often closed-source modelling software, Osprey's open-source code philosophy allows researchers to integrate state-of-the-art data processing and modelling routines, and potentially converge towards standardization of analysis. CONCLUSIONS: Osprey combines robust, peer-reviewed data processing methods into a modular workflow that is easily augmented by community developers, allowing the rapid implementation of new methods. |
DOI | 10.1016/j.jneumeth.2020.108827 |
Alternate Journal | J Neurosci Methods |
PubMed ID | 32603810 |
PubMed Central ID | PMC7477913 |
Grant List | R01 EB023963 / EB / NIBIB NIH HHS / United States R21 AG060245 / AG / NIA NIH HHS / United States K99 AG062230 / AG / NIA NIH HHS / United States P41 EB015909 / EB / NIBIB NIH HHS / United States S10 OD021648 / OD / NIH HHS / United States R01 EB016089 / EB / NIBIB NIH HHS / United States |