Susceptibility-Weighted Imaging (SWI)

Author: Steffen Bollmann, Michèle Masson-Trottier

The University of Queensland

Date: 16/03/2026

License:

Purpose¶

Susceptibility-weighted imaging (SWI) is an MRI technique that exploits differences in magnetic susceptibility between tissues to generate contrast. It is particularly useful for visualising venous vasculature, microbleeds, calcifications, and iron deposits in the brain.

This tutorial demonstrates how to:

1. Download a demo MRI dataset containing magnitude and phase images

2. Use the CLEAR-SWI Julia package within Neurodesk to compute SWI maps

3. Generate a minimum intensity projection (mIP) for enhanced vein visualisation

4. Inspect the results in an image viewer

Citation and Resources¶

Tools used in this workflow¶

CLEAR-SWI

Eckstein, K., et al. (2021). Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI. NeuroImage, 237, 118175. Eckstein et al. (2021)

Educational resources¶

• CLEAR-SWI documentation on GitHub

• Neurodesk documentation

Prerequisites¶

• A running Neurodesk environment

• Familiarity with basic terminal commands

• No additional software installation required — CLEAR-SWI is pre-installed in Neurodesk

Load software tools and download demo data¶

First, we install osfclient to download the demo dataset from the Open Science Framework, then fetch and unzip the BIDS-formatted data. We use the same demo dataset as the Unwrapping tutorial.

To do so, open a Neurodesktop session, launch a terminal and run the following commands.

The terminal in Neurodesktop.

The Neurodesktop terminal outputs after running the command.

You may wish to confirm the download was successful. You can locate the files in your neurodesktop-storage folder using the file manager. You will see the swi-demo folder containing a zipped and unzipped 01_bids folder.

The Neurodesktop file manager showing the downloaded data.

Run CLEAR-SWI¶

Write a Julia script that will perform three steps:

1. Read the magnitude and phase NIfTI files

2. Compute the SWI volume using calculateSWI

3. Generate a minimum intensity projection (mIP) using createMIP

Using the command

nano ~/neurodesktop-storage/clearswi.jl

create a file called clearswi.jl with the following contents :

using CLEARSWI

TEs = [20]
nifti_folder = joinpath(homedir(), "neurodesktop-storage/swi-demo/01_bids/sub-170705134431std1312211075243167001/ses-1/anat")
magfile = joinpath(nifti_folder, "sub-170705134431std1312211075243167001_ses-1_run-1_part-mag_T2starw.nii")
phasefile = joinpath(nifti_folder, "sub-170705134431std1312211075243167001_ses-1_run-1_part-phase_T2starw.nii")

println("Loading magnitude and phase data...")
mag = readmag(magfile);
phase = readphase(phasefile);
data = Data(mag, phase, mag.header, TEs);

println("Computing SWI...")
swi = calculateSWI(data);

println("Creating minimum intensity projection...")
mip = createMIP(swi);

outdir = joinpath(homedir(), "neurodesktop-storage/swi-demo")
println("Saving outputs...")
savenii(swi, joinpath(outdir, "swi.nii"); header=mag.header)
savenii(mip, joinpath(outdir, "mip.nii"); header=mag.header)

println("Done! Output saved to ~/neurodesktop-storage/swi-demo/")

Save the file using Ctrl+X (confirm by hitting Y followed by Enter).

The Neurodesktop terminal with the Julia script.

Open the CLEAR-SWI tool from the Neurodesk application menu.

Opening CLEAR-SWI in Neurodesktop.

Then run your newly written file from the CLEAR-SWI container terminal:

cd ~/neurodesktop-storage/
julia clearswi.jl

The CLEAR-SWI container terminal after running the script.

Inspect the results¶

Open ITK-SNAP from the Neurodesk Visualisation menu and load the output files:

• ~/neurodesktop-storage/swi-demo/swi.nii — the susceptibility-weighted image

• ~/neurodesktop-storage/swi-demo/mip.nii — the minimum intensity projection

Summary¶

In this tutorial you:

1. Downloaded a demo magnitude/phase MRI dataset from the Open Science Framework

2. Used the CLEAR-SWI Julia package to compute susceptibility-weighted images

3. Generated a minimum intensity projection for enhanced venous visualisation

4. Inspected the outputs using ITK-SNAP