# A graphical pipeline platform for MRS data processing and analysis: MRSpecLAB

## TL;DR

MRSpecLAB is a user-friendly software platform for MRS and MRSI data analysis, designed to simplify complex processing and support collaboration.

## Contribution

MRSpecLAB introduces an open-access, customizable graphical pipeline platform for MRS/MRSI data processing with minimal technical expertise required.

## Key findings

- MRSpecLAB provides an intuitive graphical interface for MRS/MRSI data analysis.
- The platform supports both predefined and customizable workflows for flexible data processing.
- MRSpecLAB aims to bridge the gap between technical experts and practitioners in MRS research.

## Abstract

Magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI), are non-invasive techniques used to quantify biochemical compounds in tissue, such as choline, creatine, glutamate, glutamine, γ-aminobutyric acid, N-acetylaspartate, etc. However, reliable quantification of MRS and MRSI data is challenging due to the complex processing steps involved, often requiring advanced expertise. Existing data processing software solutions often demand MRS expertise or coding knowledge, presenting a steep learning curve for novel users. Mastering these tools typically requires a long training time, which can be a barrier for users with limited technical backgrounds. To address these challenges and create a tool that serves researchers using MRS/MRSI with a broad range of backgrounds, we developed MRSpecLAB—an open-access, user-friendly software platform for MRS and MRSI data analysis. MRSpecLAB is designed for easy installation and features an intuitive graphical pipeline editor that supports both predefined and customizable workflows. It also serves as a platform offering standardized pipelines while allowing users to integrate in-house functions for additional flexibility. Importantly, MRSpecLAB is envisioned as an open platform beyond the MRS community, bridging the gap between technical experts and practitioners. It facilitates contributions, collaboration, and the sharing of data workflows and processing methodologies for diverse MRS/MRSI applications, supporting reproducibility practices.