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Quantitative susceptibility mapping allows the determination of a basic physical property in vivo. Early concepts for QSM were introduced a decade ago and more refined methods have been proposed recently to allow the calculation of magnetic susceptibility from a single orientation, in a clinical setup, and with low reconstruction artefacts. Therefore, QSM has attracted a high number of researchers and is utilized in clinical studies of neurological disorders and is increasingly becoming a topic of research outside the brain.
 

TGV QSM Algorithm 

Python source code (68 MB) for total generalized variation (TGV) based QSM including 3D EPI test data.
Described in: ** Langkammer, C; Bredies, K; Poser, BA; Barth, M; Reishofer, G; Fan, AP; Bilgic, B; Fazekas, F; Mainero; C; Ropele, S
Fast Quantitative Susceptibility Mapping using 3D EPI and Total Generalized Variation.
Neuroimage. 2015 May 1;111:622-30. doi: 10.1016/j.neuroimage.2015.02.041. PubMed 

Currently we are working on a GPU accelerated version using OpenCL. If you are interested in testing please contact Christian.

 

QSM Reconstruction Challenge 2016

Purpose

The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.

Methods

Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations.

Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions.

Results

Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance.

Conclusion

Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria.

Downloads

MRI data and Matlab source code: qsm_recon_challenge_2016.zip (240 MB).
Manuscript of the QSM reconstruction challenge: mrm26830.pdf (MRM webpage:  http://onlinelibrary.wiley.com/doi/10.1002/mrm.26830/full).

 

4th QSM Workshop in Graz

We thank all participants of the 4th International Workshop on MRI Phase Contrast & Quantitative Susceptibility Mapping in Graz!
Homepage of the QSM workshop 2016.