Abstract #1868

Reproducibility Made Easy: A Tool for Methodological Transparency & Efficient Standardized Reporting based on the proposed MRSinMRS Consensus

Antonia Susnjar¹, Antonia Kaiser², Gianna Nossa³, Dunja Simicic^4,5, and Aaron Gudmundson^4,5

¹Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Institute for Innovation in Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ²Animal Imaging and Technology core, CIBM Center for Biomedical Imaging, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland, ³School of Health Sciences, Purdue University, West Lafayette, IN, United States, ⁴Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Keywords: Spectroscopy, Spectroscopy, Reproducibility

Motivation: Several consensus papers by MRS experts have addressed data collection, analysis, and reporting standards. Despite this, the usage of the MRSinMRS standardized reporting criteria remain sparsely utilized, impeding research rigor and reproducibility.

Goal(s): To overcome this, the ‘’Reproducibility Made Easy’’ software automates table population and methods section generation, streamlining the process with a single raw dataset, removing manual data entry.

Approach: We propose a tool that automatically creates a table from a single MRS raw data file, to make the process of adhering to reproducibility standards easy.

Results: The open-source ‘’Reproducibility Made Easy’’ tool can be found here: https://github.com/agudmundson/mrs_in_mrs.

Impact: Integrating the MRSinMRS Consensus Table faces challenges in parameter location within DICOM headers or MRS raw files due to nomenclature variations. "Reproducibility Made Easy" software addresses these issues, enhancing methodological transparency and standardization in research, aligning with MRSinMRS consensus principles.

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