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QSMxT v9 is a ground-up rewrite in Rust. Coming from the Python version (8.x)? See what changed →

Quick start

There are two ways to drive QSMxT: interactively in the TUI (recommended) or noninteractively on the command line. Both take you from raw data to QSM maps.

Launch the interactive terminal interface and do everything on screen — convert DICOMs, review the classification, configure the pipeline, and run it:

Terminal window
qsmxt tui

The TUI walks you through each step, with menus for every algorithm and sensible defaults pre-filled. It’s the best way to start, especially with a new dataset. See Running interactively.

Prefer to script it? The same workflow in two commands.

Terminal window
qsmxt dicom-convert /path/to/dicoms study/bids

QSMxT scans the directory recursively, classifies each series automatically (gradient-echo magnitude and phase, echo count, individual coils, combined/derived reconstructions), and writes a BIDS dataset. Add --dry-run to preview the classification without writing anything. See DICOM → BIDS.

(Already have a BIDS dataset? Skip to step 2.)

Terminal window
qsmxt run study/bids

QSMxT discovers every phase/magnitude run in the dataset and reconstructs a QSM map for each, writing results to study/bids/derivatives/qsmxt.rs/. With no configuration it uses sensible defaults: ROMEO unwrapping, V-SHARP background removal, and RTS dipole inversion. Pick algorithms inline or process a subset:

Terminal window
qsmxt run study/bids --qsm-algorithm rts --include "sub-01*"

See Running noninteractively.

Want to experiment first without installing anything? qsmbly runs the same reconstruction algorithms entirely in your browser.

Outputs land under derivatives/qsmxt.rs/ as BIDS-compliant NIfTIs — one QSM map per run, plus any supplementary outputs you enabled (SWI, T2*, R2*, RSS-combined magnitude). A references.txt lists citations for the exact methods your data and settings used.