QSMbly

QSMbly Quantitative Susceptibility Mapping

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Drop NIfTI, DICOM, or JSON files

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Tesla

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Flip axes if needed, preview, then inspect every slice before applying. iPreview the mask on the input image. Repair replaces the mask's orientation and origin with the image's. It cannot correct different anatomy, cropping, or a shifted mask.

SWI

T2* / R2*

Echo 1/1
Status Drag and drop files

Pipeline Settings

QSM Pipeline

Paper Code

TFI is single-step: it inverts the total field directly (its own background removal). Larger preconditioner suits data with strong background fields (e.g. air near sinuses).

Paper Paper Code
Paper Code

⚠ QSMART needs a tight brain mask. Build the mask with BET in the masking step — a loose threshold mask leaks non-brain signal into the dipole inversion and causes heavy streaking artifacts.

Stage 1: Whole ROI
Stage 2: Tissue Only
SDF Parameters
Vasculature Detection (Frangi)

Parameters in mm - auto-scaled to voxels

Dipole Inversion

Removes spatially-varying phase offset from B1 transmit/receive and eddy currents.
MCPC-3D-S ASPIRE Code
Smoothing Sigma (voxels)

Reference
Removes linear phase artefact caused by bipolar readout gradients.

Phase Unwrapping

Paper Code
Paper Code

Multi-Echo Fitting

Voxels whose echo-fit residual lands above this percentile are flagged unreliable and excluded. Set to 0 to keep every voxel.

Background Removal

Overridden by MEDI with SMV
Paper Code
Paper Code
Paper
Paper Code
Paper Code
Paper Code
Paper
Paper

Dipole Inversion

Paper Code
Paper Code
Paper Code
Paper Code
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Paper Code
Paper Code
Paper Code
Paper Code
Paper Code
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Paper Code

QSM Referencing

Zero-centers susceptibility values by subtracting the mean of all masked voxels. Recommended for most applications.

BET Settings

Brain Extraction Parameters

BET's surface model is tuned for the human brain in millimetres, so on a mouse brain its search distances span the whole head. Mouse BET multiplies the voxel sizes by the scale factor below before running BET, which brings the brain to roughly human size; the mask comes back on the original grid.

Multiplier applied to the voxel sizes for BET (10 for mouse and rat)
Smaller values give larger brain estimate (0.0-1.0)
0.5
More iterations = smoother result but slower (100-2000)
3 = 642 vertices, 4 = 2562 vertices, 5 = 10242 vertices
Erosion steps to apply after mask generation (0 = none)

Run this command with QSMxT to process a full BIDS dataset with your current settings:


        

Auto-generated methods section with citations based on your pipeline settings:

SWI Settings

About QSMbly

QSMbly
QSMbly

Quantitative Susceptibility Mapping in the browser

QSMbly is a browser-based application for Quantitative Susceptibility Mapping (QSM). It runs entirely in your web browser using WebAssembly, providing near-native performance without requiring any software installation or data upload.

QSMbly runs the same reconstruction code as QSMxT, the command-line QSM pipeline: both are built on QSM.rs. Try methods here, then scale up with QSMxT when you have a whole study to process.

Version

QSMbly (web UI)
QSM.rs (core Rust library)

Privacy

Your data never leaves your device.

QSMbly runs entirely in your web browser using WebAssembly. All processing occurs locally on your machine.

What this means

  • No data is uploaded to any server or cloud service
  • After the page loads, an internet connection is needed only for the optional downloads listed below
  • Sensitive patient data remains on your computer at all times
  • There is no need to anonymise, deface, or otherwise de-identify your data before use

How it works

QSMbly compiles its processing algorithms from Rust to WebAssembly (WASM), which runs natively in the browser at near-native speed. Your NIfTI files are read directly into browser memory, processed locally, and results are displayed without any network transfer.

Analytics

QSMbly uses Cloudflare Web Analytics to collect anonymous page-view statistics (e.g. number of visitors, country, referrer). This does not use cookies, does not track individual users, and does not have access to any of your data or processing results. It helps us understand how many people use QSMbly and where they come from.

Other network requests

QSMbly does not use cookies, fingerprinting, or any other tracking. Besides the application itself and the analytics beacon, the page downloads code and data from these services; none of them receives your images:

  • The Inter font from Google Fonts, and the Tagify library from the unpkg CDN
  • The QSMxT project navigation bar from qsmxt.github.io
  • Deep-learning model weights from Hugging Face, the first time you use a deep-learning method (then cached in your browser)
  • Example data from Hugging Face, if you choose to load it
  • The dicompare protocol checker from dicompare.neurodesk.org, which in turn loads Python packages from jsDelivr and PyPI; it reads your DICOM headers locally

Because of these downloads, deep-learning methods and dicompare need an internet connection the first time they are used.

dicompare Report