Standardized MRSI Data Analysis Platform (MAPS)
Biomedical Engineering
Carolina Avila, Dimitra Manatou, Rohit Panicker, Anneth Stephen, Christopher Zapata
Abstract
Magnetic Resonance Spectroscopic Imaging (MRSI) provides valuable metabolic information for diagnosing and monitoring neurological conditions; however, its clinical and research use is limited by fragmented, non-standardized analysis workflows. Differences in scanner vendor formats require users to manually convert files, resolve metadata inconsistencies, and rely on multiple software tools, slowing data interpretation, increasing error risk, and limiting accessibility for clinicians and researchers.
To address this need, we developed M.A.P.S. (MRSI Analysis and Python Spectroscopy), a vendor-agnostic software medical device for real-time MRSI data analysis and visualization. Built as a plugin within Napari, the platform integrates anatomical imaging with voxel-level spectral data in a single interface, enabling intuitive exploration of metabolic information across brain regions. M.A.P.S. supports multiple data formats, including DICOM spectroscopy objects, NIfTI files, and vendor-specific exports, eliminating the need for preprocessing or external conversion. In addition to the graphical interface, a command-line interface (CLI) enables batch processing, automated data loading, and integration into existing pipelines, improving scalability for research and clinical workflows.
The platform includes a universal MRSI data loader, NIfTI export functionality, and interactive 2D/3D visualization. Real-time spectral updates allow dynamic exploration of voxel-specific data, improving efficiency and reducing analysis time. Initial validation demonstrates compatibility across 4–5 MRSI data formats, with consistent representation and smooth real-time performance on millisecond-to-second timescales using standard computing systems.
By standardizing MRSI data handling and reducing technical barriers, M.A.P.S. has the potential to streamline clinical workflows, enhance diagnostic efficiency, and support broader adoption of spectroscopy in clinical and research environments.