Console
Quantitative Susceptibility Mapping, in your browser.
Load your phase and magnitude images, pick your algorithms, and reconstruct susceptibility maps. Nothing to install, and your data stays on your computer.
Your data never leaves your device, so there's no need to de-identify scans first.
No Python, MATLAB, or Docker. The whole pipeline is compiled to WebAssembly.
Phase unwrapping, background field removal, and two dozen dipole inversion methods. Mix and match to suit your data.
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.
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).
⚠ 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.
Parameters in mm - auto-scaled to voxels
HD-BET runs a 30-million-parameter neural network over every overlapping patch of the volume. It is the most accurate option here, and by far the slowest — expect several minutes. Progress is reported per patch, and Stop cancels it at any point.
First run downloads 123 MB of model weights (CC-BY-NC-4.0). They are cached in your browser afterwards.
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:
New here? Take a quick guided tour and we'll walk you through building your first susceptibility map, step by step.
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 entirely in your web browser using WebAssembly. All processing occurs locally on your machine.
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.
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.
Beyond the lightweight analytics above, QSMbly does not use cookies, fingerprinting, or any other tracking. The only network requests are loading the application itself and the analytics beacon.
Stewart, A.W. (2026). "QSMbly: Browser-based Quantitative Susceptibility Mapping."
https://github.com/astewartau/qsmblyQSMbly implements algorithms from the following publications. Please cite the relevant papers if you use these methods in your research.
Smith, S.M. (2002). "Fast robust automated brain extraction." Human Brain Mapping, 17(3):143-155.
DOI: 10.1002/hbm.10062Dymerska, B., Eckstein, K., Bachrata, B., Siow, B., Trattnig, S., Shmueli, K., Robinson, S.D. (2021). "Phase Unwrapping with a Rapid Opensource Minimum Spanning TreE AlgOrithm (ROMEO)." Magnetic Resonance in Medicine, 85(4):2294-2308.
DOI: 10.1002/mrm.28563Schofield, M.A., Zhu, Y. (2003). "Fast phase unwrapping algorithm for interferometric applications." Optics Letters, 28(14):1194-1196.
DOI: 10.1364/OL.28.001194Eckstein, K., Dymerska, B., Bachrata, B., Bogner, W., Poljanc, K., Trattnig, S., Robinson, S.D. (2018). "Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)." Magnetic Resonance in Medicine, 79:2996-3006.
DOI: 10.1002/mrm.26963Eckstein, K., Trattnig, S., Robinson, S.D. (2019). "A Simple Homogeneity Correction for Neuroimaging at 7T." Proc. ISMRM 27th Annual Meeting.
Wu, B., Li, W., Guidon, A., Liu, C. (2012). "Whole brain susceptibility mapping using compressed sensing." Magnetic Resonance in Medicine, 67(1):137-147.
DOI: 10.1002/mrm.23000Schweser, F., Deistung, A., Lehr, B.W., Reichenbach, J.R. (2011). "Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase." NeuroImage, 54(4):2789-2807.
DOI: 10.1016/j.neuroimage.2010.10.070Sun, H. and Wilman, A.H. (2013). "Background field removal using spherical mean value filtering and Tikhonov regularization." Magnetic Resonance in Medicine, 71(3):1151-1157.
DOI: 10.1002/mrm.24765Wen, Y., Zhou, D., Liu, T., Spincemaille, P., Wang, Y. (2014). "An iterative spherical mean value method for background field removal in MRI." Magnetic Resonance in Medicine, 72(4):1065-1071.
DOI: 10.1002/mrm.24998Liu, T., Khalidov, I., de Rochefort, L., Spincemaille, P., Liu, J., Tsiouris, A.J., Wang, Y. (2011). "A novel background field removal method for MRI using projection onto dipole fields." NMR in Biomedicine, 24(9):1129-1136.
DOI: 10.1002/nbm.1670Zhou, D., Liu, T., Spincemaille, P., Wang, Y. (2014). "Background field removal by solving the Laplacian boundary value problem." NMR in Biomedicine, 27(3):312-319.
DOI: 10.1002/nbm.3064Li, W., Wu, B., Liu, C. (2014). "Integrated Laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping." NMR in Biomedicine, 27(2):219-227.
DOI: 10.1002/nbm.3056Li, W., Wu, B., Liu, C. (2015). "iHARPERELLA: an improved method for integrated 3D phase unwrapping and background phase removal." Proc. ISMRM 23, p.3313.
Yaghmaie, N., Syeda, W., et al. (2021). "QSMART: Quantitative Susceptibility Mapping Artifact Reduction Technique." NeuroImage, 231:117701.
DOI: 10.1016/j.neuroimage.2020.117701Shmueli, K., de Zwart, J.A., van Gelderen, P., Li, T.Q., Dodd, S.J., Duyn, J.H. (2009). "Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data." Magnetic Resonance in Medicine, 62(6):1510-1522.
DOI: 10.1002/mrm.22135Bilgic, B., et al. (2014). "Fast image reconstruction with L2-regularization." Journal of Magnetic Resonance Imaging, 40(1):181-191.
DOI: 10.1002/jmri.24365Bilgic, B., Fan, A.P., Polimeni, J.R., et al. (2014). "Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection." Magnetic Resonance in Medicine, 72(5):1444-1459.
DOI: 10.1002/mrm.25029Kames, C., Wiggermann, V., Rauscher, A. (2018). "Rapid two-step dipole inversion for susceptibility mapping with sparsity priors." NeuroImage, 167:276-283.
DOI: 10.1016/j.neuroimage.2017.11.018Kames, C., Wiggermann, V., Rauscher, A. (2018). "Rapid two-step dipole inversion for susceptibility mapping with sparsity priors." NeuroImage, 167:276-283.
DOI: 10.1016/j.neuroimage.2017.11.018Liu, T., Liu, J., de Rochefort, L., et al. (2011). "Morphology enabled dipole inversion (MEDI) from a single-angle acquisition." Magnetic Resonance in Medicine, 66(3):777-783.
DOI: 10.1002/mrm.22816Li, W., Wang, N., Yu, F., et al. (2015). "A method for estimating and removing streaking artifacts in quantitative susceptibility mapping." NeuroImage, 108:111-122.
DOI: 10.1016/j.neuroimage.2014.12.043Langkammer, C., Bredies, K., Poser, B.A., et al. (2015). "Fast quantitative susceptibility mapping using 3D EPI and total generalized variation." NeuroImage, 111:622-630.
DOI: 10.1016/j.neuroimage.2015.02.041Chatnuntawech, I., McDaniel, P., et al. (2017). "Single-step quantitative susceptibility mapping with variational penalties." NMR in Biomedicine, 30(4):e3570.
DOI: 10.1002/nbm.3570Yaghmaie, N., Syeda, W., Wu, C., Zhang, Y., Zhang, T., Burrows, E.L., Moffat, B.A., Wright, D.K., Glarin, R., Kolbe, S., Johnston, L.A. (2021). "QSMART: Quantitative Susceptibility Mapping Artifact Reduction Technique." NeuroImage, 231:117701.
DOI: 10.1016/j.neuroimage.2020.117701Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A. (1998). "Multiscale vessel enhancement filtering." MICCAI'98, LNCS vol 1496, 130-137.
DOI: 10.1007/BFb0056195Meyer, M., Desbrun, M., Schroder, P., Barr, A.H. (2003). "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds." Visualization and Mathematics III, 35-57.
DOI: 10.1007/978-3-662-05105-4_2Deistung, A., Schweser, F., Reichenbach, J.R. (2017). "Overview of quantitative susceptibility mapping." NMR in Biomedicine, 30(4):e3569.
DOI: 10.1002/nbm.3569Schweser, F., Deistung, A., Reichenbach, J.R. (2016). "Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM)." Zeitschrift fur Medizinische Physik, 26(1):6-34.
DOI: 10.1016/j.zemedi.2015.10.002Harada, T., Kudo, K., Fujima, N., et al. (2022). "Quantitative Susceptibility Mapping: Basic Methods and Clinical Applications." RadioGraphics, 42(4):1161-1176.
DOI: 10.1148/rg.210054The following repositories were used as references: