qmrust

It is qMRLab (https://qmrlab.org) written in Rust, so it is fast, and the fitting code is portable: the same routines run on the command line and, compiled to WebAssembly, inside a web browser with no server.

qmrust is a growing library of models, each one self-contained so a new model can be added without disturbing the others. It fits:

• Inversion recovery T1
• Quantitative magnetization transfer (qMT-SPGR), with the Ramani and Sled-Pike sub-models

More are on the way, and the project is built to make adding them simple.

Getting started

Check out docs: qmrlab.org/qmrust

Build the tool:

cargo build --release

The binary lands at target/release/qmrust. Run cargo install --path . to call qmrust from anywhere.

Fit a dataset:

qmrust fit --bids-dir path/to/dataset \
  --config recipes/bids/irt1_config.yaml \
  --output-dir results

qmrust reads BIDS datasets directly, taking each scan's acquisition details from its sidecar. When your data is not in BIDS, point it at plain NIfTI or qMRLab .mat files instead and the acquisition details come from the config. The recipes/ folder holds ready-to-edit example configs, one per model, grouped by how you feed in the data. Run qmrust fit --help for the full list of inputs.

Simulating

You can also generate a signal from known parameters, add noise, and fit it back. This helps you check a model or see how reliably a parameter can be recovered.

qmrust sim single-voxel --config recipes/sim/qmt_sim_ramani.yaml --output result.json

Learning more

• recipes/README.md walks through the example configs and how to run BIDS and non-BIDS data.
• docs/ covers the available models, the data pipeline, and how to add a model of your own.

Background

qmrust grows out of qMRLab, the MATLAB toolbox for quantitative MRI. It reimplements selected models natively so they run quickly and in more places, and its results are made to line up with qMRLab's for validation. Values follow BIDS conventions in SI units, so a T1 map is in seconds rather than qMRLab's milliseconds.