Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data

Georg Oeltzschner; Helge J Zöllner; Steve C N Hui; Mark Mikkelsen; Muhammad G Saleh; Sofie Tapper; Richard A E Edden

doi:10.1016/j.jneumeth.2020.108827

Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data

J Neurosci Methods. 2020 Sep 1:343:108827. doi: 10.1016/j.jneumeth.2020.108827. Epub 2020 Jun 27.

Authors

Georg Oeltzschner¹, Helge J Zöllner², Steve C N Hui², Mark Mikkelsen², Muhammad G Saleh², Sofie Tapper², Richard A E Edden²

Affiliations

¹ Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States. Electronic address: goeltzs1@jhmi.edu.
² Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States.

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.

Keywords: Linear-combination modelling; Magnetic resonance spectroscopy; Pre-processing; Quantification; Tissue correction.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Ecosystem*
Healthy Volunteers
Humans
Magnetic Resonance Imaging*
Magnetic Resonance Spectroscopy
Software

Abstract

Publication types

MeSH terms

Grants and funding