Introduction

Magnetic resonance spectroscopy (MRS) is a non-invasive method that assesses tissue metabolic profiles which is widely used in research and increasingly applied in the clinical context.3 Decades of progress in MRS protocol development, data processing, and quantification have led to consensus papers, including the Minimum Reporting Standards in MRS (MRSinMRS) which is a reporting standard for MRS studies.1 This agreed-upon reporting standard includes a checklist and supplementary table that provides readers with all the experimental details needed to reproduce a study. Despite this, the usage of this standardized reporting criteria for MRS studies remains sparsely utilized, impeding research rigor and reproducibility. One of the biggest challenges in the adoption of the MRSinMRS, is locating parameters within DICOM headers or MRS raw files due to nomenclature variations and complexity of the data formats.The MRSinMRS consensus paper, published in 2021, has gained immediate traction receiving a notable 102 citations. Though only 43 references incorporated the MRSinMRS table, while the remaining 59 citations only acknowledged the paper (Figure 3). To address discrepancy in implementing the MRSinMRS table, we present the "Reproducibility Made Easy" toolbox which automatically finds the experimental parameters required for the MRSinMRS table and creates a compact and informative methods section to be used in publications.

Methods

"Reproducibility Made Easy" is designed to be a standalone application to generate the MRSinMRS table and an MRS methods section used for publications. The Python-based software (v. 3.11) is open-source and requires no experience by operating through an intuitive graphical user interface (GUI) built using Tkinter.2 The application is operating-system (OS) agnostic, meaning it operates uniformly across platforms. One MRS raw data file is required to read in acquisition parameters (e.g.: repetition time (TR), echo time (TE)) and exports a comma separated values (.csv) file that provides the MRSinMRS table (Figure 1) as well as a text document (Latex, word, and PDF format) with a completed and referenced Methods section as shown in Figure 1.

Results

The "Reproducibility Made Easy" software toolbox is freely available for download at https://github.com/agudmundson/mrs_in_mrs. The application was tested using example .rda, .dat, .spar, .sdat, fid and ser data inputs for GE, Siemens, Phillips and Bruker MRS data formats. The application successfully translated the test data acquired with conventional MRS sequences to the output table and methods section. It allows inter-center credibility by fostering a standardized approach and is independent from any proprietary dependencies. Moreover, it is flexible and could be configured to specific research needs or integrated into existing MRS analysis software, by reading in the header file of any of the above-mentioned protocols and vendors. Lastly, it is adaptable as it could be refined with its own implementations, and source code could be improved in accordance with community recommendations and needs.

Discussion and Conclusion

The "Reproducibility Made Easy" application offers a robust solution for researchers and clinicians to comprehensively document study parameters. It automates table population from a single datafile, facilitating result replication and method evaluation. The tool also generates a simplified but comprehensive methods section, ensuring reporting accuracy and result interpretability. Current reporting standards require manual table creation, but finding the necessary parameters and details can be challenging, especially for newcomers encountering parameter nomenclature variations among vendors and technical difficulties. Our application tackles this by auto-populating parameters based on vendor detection, reducing errors and inconsistencies. This community project addresses the hurdle of technical as well as coding expertise by creating a user-friendly application. ”Reproducibility Made Easy” originated as a collaborative initiative among trainees in the MRS study group, aimed at streamlining reporting standards for both fellow colleagues and newcomers in the field. Future plans involve extending reporting tables to multiple sequences, nuclei, and MRS Imaging. While the MRS field's progress and increased number of studies are promising, method reporting remains a concern. To address this, we developed an open-source processing tool, enhancing transparency and research credibility of the field, which is easy to integrate with existing and to be developed tools. With its user-friendly interface and adherence to the MRSinMRS guidelines, "Reproducibility Made Easy" empowers scientists and clinicians to easily share their methodologies and make their research findings more accessible to the scientific community.

Acknowledgements

Our gratitude extends to the organizers and hosts of the inaugural MRS Hackathon 2023, who afforded us both space and the chance to interact with a diverse group of spectroscopists. This collaborative environment enabled us to conceive and actualize the Reproducibility made easy application. Their support was instrumental in converting our concept into a concrete solution, addressing the gap of reporting standards in the MRS field, which emerged during discussions at the MRS Workshop 2022 in Lausanne, Switzerland.