Magnetic resonance spectroscopic imaging (MRSI) produces maps of neurometabolite concentrations across the brain, but practices for downstream processing after spectral fitting remain heterogeneous across research groups. MRSIPrep is presented as an open-source, modular, and reproducible post-quantification framework specifically designed for whole-brain MRSI. Its primary aim is to standardize the steps that follow metabolite quantification so that outputs are comparable, transparent, and ready for a range of downstream analyses.
The authors emphasize that, while spectral fitting and quantification tools have matured, the lack of a standardized post-quantification workflow has limited reproducibility and comparability of MRSI results. MRSIPrep addresses this gap by implementing a consistent sequence of processing steps, automated quality-control reporting, and derivative generation suitable for voxelwise, regional, and connectomics-style analyses.
MRSIPrep standardizes several key downstream procedures applied to quantified metabolite maps and their associated quality metrics. These procedures include automated quality control, tissue correction, spatial normalization, and atlas projection. By organizing these operations into a single, modular pipeline, MRSIPrep reduces variability introduced by ad hoc or lab-specific processing choices.
The framework accepts post-quantification metabolite maps and quality metrics as inputs and applies systematic processing steps to produce harmonized derivatives. Emphasis is placed on reproducibility and modularity so users can apply the entire pipeline or adapt components to their specific requirements.
MRSIPrep produces multiple derivative outputs intended to support varied analytic approaches. Outputs include voxelwise corrected metabolite maps, region-level summaries derived through atlas projection, and formatted datasets prepared for connectomics or network analyses. These derivative products are accompanied by the quality metrics used during processing, enabling transparent downstream interpretation and selection of data for analysis.
By providing both fine-grained voxelwise outputs and aggregated regional outputs, the pipeline supports investigators interested in local metabolite distributions as well as those focused on larger-scale regional or network-level investigations.
A central component of the framework is automated quality control and report generation. MRSIPrep aggregates quality metrics produced during quantification and applies standardized criteria and reporting formats so users can rapidly assess data quality across voxels and subjects. The pipeline also produces visual and textual summaries intended to facilitate reproducible decision making about inclusion, exclusion, or further correction of metabolite data.
Automated QC reports serve both as documentation for analytic choices and as an audit trail supporting transparency and reproducibility of published analyses.
The architecture of MRSIPrep is described as modular, which allows components to be reused, adapted, or replaced as required by specific datasets or downstream goals. Modular design supports reproducibility by enabling standard configurations while permitting customization when justified.
Reproducibility is further supported through packaging of processing steps into a cohesive workflow and by providing code and documentation for community access. The authors highlight that the framework is intended to work across different acquisition protocols and quantification outputs, although implementation details and parameters are provided in the project documentation rather than in the preprint text.
The preprint reports that the authors demonstrate MRSIPrep's utility for reproducible MRSI analysis across datasets, acquisition protocols, and downstream applications. This indicates the pipeline was applied to varied inputs to show its flexibility and to support its use as a common post-quantification standard for whole-brain MRSI. Specific datasets, sample sizes, or quantitative performance comparisons were not reported in the abstract of the preprint.
MRSIPrep is released as an open-source project. The preprint provides links to the GitHub repository and to the online documentation for the project. These resources are intended to allow other groups to inspect, adopt, and contribute to the pipeline while following documented usage and configuration guidelines.
Users interested in adopting MRSIPrep should consult the project repository and documentation for installation instructions, supported inputs, configuration options, and examples. Links reported in the preprint include the GitHub repository (https://github.com/MRSI-Psychosis-UP/MRSIPrep) and the documentation site (https://mrsiprep.readthedocs.io/en/stable/).
This work is presented as a preprint on bioRxiv and has not undergone peer review. The abstract does not report specific quantitative benchmarks, detailed processing parameters, or exhaustive validation results; readers should refer to the full preprint and the project documentation for implementation details and any reported evaluations.
A competing interest statement notes that one author is employed by Siemens Healthcare and contributes to research and development initiatives; that author declares no fiduciary responsibilities for Siemens Healthcare. All other authors declared no competing interests. Funding sources cited in the preprint include grants from the Swiss National Science Foundation and several foundations.
Researchers adopting MRSIPrep should review the full preprint and available code/documentation to understand the pipeline's assumptions, default parameters, and validation evidence before integrating it into production workflows.