Fully automated open-source analysis and interactive visualization of magnetic resonance spectroscopic imaging (MRSI) data in Osprey-MRSI
- 1The Johns Hopkins University School of Medicine
Abstract
Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.
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BibTeX
@misc{zoellner2026fully,
title = {{Fully automated open-source analysis and interactive visualization of magnetic resonance spectroscopic imaging (MRSI) data in Osprey-MRSI}},
author = {H. J. Zoellner and A. R. Craven and V. Karlaftis and W. T. Clarke and D. K. Senapati and D. D. M. Lin and G. Oeltzschner and P. B. Barker},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.02.748934},
url = {https://www.biorxiv.org/content/10.64898/2026.09.02.748934v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Fully automated open-source analysis and interactive visualization of magnetic resonance spectroscopic imaging (MRSI) data in Osprey-MRSI
AU - H. J. Zoellner
AU - A. R. Craven
AU - V. Karlaftis
AU - W. T. Clarke
AU - D. K. Senapati
AU - D. D. M. Lin
AU - G. Oeltzschner
AU - P. B. Barker
PY - 2026
DA - 2026/09/02
PB - bioRxiv
DO - 10.64898/2026.09.02.748934
UR - https://www.biorxiv.org/content/10.64898/2026.09.02.748934v1
AB - Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.
ER -