GRE and QSM average 7T model


View online and download the model in MINC or NiFTI format here:

GRE model

greModel_L14_gre-7T-weightedMeanOfAllEchoes-asym-mincanon_v0.8 [ View model online | mnc | nii ]

QSM model

qsmModel_L14_gre-7T-weightedMeanOfAllEchoes-asym-mincanon_v0.8 [ View model online | mnc | nii ]

Citing this model:

Bollmann, Steffen, Andrew Janke, Lars Marstaller, David Reutens, Kieran O’Brien, and Markus Barth. “GRE and QSM Average 7T Model” January 1, 2017. doi:10.14264/uql.2017.178.


References describing this model:

Bollmann, Steffen, Simon Daniel Robinson, Kieran O’Brien, Viktor Vegh, Andrew Janke, Lars Marstaller, David Reutens, and Markus Barth. “The Challenge of Bias-Free Coil Combination for Quantitative Susceptibility Mapping at Ultra-High Field.” Magnetic Resonance in Medicine, March 1, 2017, n/a-n/a. doi:10.1002/mrm.26644.

Janke AL, O’Brien K, Bollmann S, Kober T, Marstaller L, Barth M. A 7T Human Brain Microstructure Atlas by Minimum Deformation Averaging at 300um. In 24th Annual ISMRM Scientific Meeting and Exhibition, Singapore.

Acquisition details

QSM: 29 (14 female, 26.6±3.8yr) individuals were imaged using a multiple echo gradient recalled echo 3D whole brain dataset. TR=25ms, TE=4.4,7.25,10.2,13.25,16.4, 19.65,23ms flip angle=13, FOV 210x181.5x120mm, matrix=280x242x160, GRAPPA=2. The phase data was combined using COMPOSER [8] and susceptibility processing was performed using total generalized variation (TGV) [9], which incorporates phase unwrapping, background field removal and dipole inversion in a single step.

  1. Robinson SD, Wolfgang Bogner, Barbara Dymerska, Pedro Cardoso, Günther Grabner, Xeni Deligianni, et al. COMbining Phased array data using Offsets from a Short Echo-time Reference scan (COMPOSER). ISMRM 23rd Annual Meeting & Exhibition. 2015
  2. Langkammer, C., Bredies, K., Poser, B.A., Barth, M., Reishofer, G., Fan, A.P., Bilgic, B., Fazekas, F., Mainero, C., Ropele, S., 2015. Fast quantitative susceptibility mapping using 3D EPI and total generalized variation. NeuroImage 111, 622–630. doi:10.1016/j.neuroimage.2015.02.041