Resomapper: An Integrated Workflow for Quantitative Multiparametric MRI in Preclinical Studies

Quantitative magnetic resonance imaging (qMRI) provides insights into tissue microstructure and physiology, making it a tool in preclinical research. However, its processing often requires coding expertise.

We present Resomapper, an open-source, cross-platform tool designed to simplify multiparametric MRI analysis. Resomapper integrates libraries into a user-friendly workflow that supports T₁, T₂, and T₂* relaxometry mapping, magnetization transfer imaging, and diffusion tensor imaging. It incorporates preprocessing steps -including denoising, Gibbs artifact removal, and bias correction- and enables interactive and automated processing.

Resomapper was applied to a multiparametric 7T dataset of healthy mouse brain, generating T₁, T₂, T₂*, magnetization transfer ratio and diffusion tensor maps. The values fell within the ranges reported in the literature and reproduced the differences between grey and white matter regions, illustrating its use in a preclinical study.

Resomapper distinguishes itself from qMRI tools not through novel algorithms, but through its integration of established, validated libraries into a single, streamlined pipeline for data conversion, preprocessing and parametric mapping. Maps showed excellent agreement with qMRLab for T₂ and were consistent with DSI Studio for mean and axial diffusivity, while systematic differences in radial diffusivity and fractional anisotropy were attributable to the tensor fitting methods. Its design prioritizes reproducibility and standardization, and its Python implementation makes the tool straightforward to install, inspect, and adapt, even for researchers without extensive coding backgrounds.

By improving accessibility, reproducibility, and standardization, Resomapper aims to facilitate broader adoption of qMRI in preclinical imaging despite current methodological challenges.