End-to-end pipeline
Masking, phase unwrapping, echo combination, background field removal, dipole inversion, and referencing — orchestrated for you by a single self-contained binary, with defaults that work on human brain GRE data as they come.
curl -fsSL https://raw.githubusercontent.com/QSMxT/QSMxT/main/install.sh | shirm https://raw.githubusercontent.com/QSMxT/QSMxT/main/install.ps1 | iexSee the installation guide for other options, including building from source.
End-to-end pipeline
Masking, phase unwrapping, echo combination, background field removal, dipole inversion, and referencing — orchestrated for you by a single self-contained binary, with defaults that work on human brain GRE data as they come.
50+ algorithms
Swap the method at any stage: four masking recipes, eight background-field removers, 18 classical and 11 deep-learning dipole inversions, and eight source-separation methods — all provided by QSM.rs.
Interactive TUI
Convert, configure, and run from a polished terminal interface — the recommended way to drive QSMxT, no config file required.
BIDS-native
Finds phase and magnitude, reads JSON sidecars, and writes compliant
derivatives to derivatives/qsmxt/. Starting from DICOMs, dicom-convert
classifies the series for you.
Built for scale
Disk caching skips completed steps on re-runs, memory-aware parallelism fills your cores, and SLURM scripts fan out across a cluster.
If QSMxT is useful in your research, please cite:
Stewart AW, Robinson SD, O’Brien K, et al. “QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping.” Magnetic Resonance in Medicine 87.3 (2022): 1289–1300. doi.org/10.1002/mrm.29048
Every run also writes a references.txt listing citations for the specific
methods your data and settings exercised.