T1_map in nighres (7T)

Dear all,
Is T1 map based on Siemens’s ICE program output? or it being calculated based on the UNI images?

Furthermore, using mp2rage_skullstripping
Testing my data, as well as in the example data, it does not do the best job…(for example, missing part of the temporal lobe).

I think your T1 can be either generated “by the scanner” or reconstructed from UNI. I don’t think there is any general rule.

The temporal lobe issue is a known thing with nighres but it is more due to the SNR you get in thos regions.

I would post an issue on the repo just to be sure: https://github.com/nighres/nighres/issues

Thank you, is there any guidelines how to reconstructed it from UNI?

My subject had dielectric pads.
Using fsl BET on the INV2 image (output of binary brain mask) and then apply this mask to the UNI give better results (it include the temporal lobe…)

Maybe have a look at the BIDS appendix on MP2RAGE: Quantitative MRI - Brain Imaging Data Structure v1.6.1-dev

Main softwares I know of for T1 map creation:

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Dear @tali.weiss ,

The guideline to reconstruct T1 from UNI can be found in:

  • Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P.-F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage, 49(2), 1271–1281. https://doi.org/10.1016/j.neuroimage.2009.10.002


Other than this, deriving a brain mask from the INV2 image totally makes sense, as in this image the brain is bright and outside of the brain is dark, making it easy for the BET algorithm to fin the edges of the brain correctly.

Marques scripts are there by the way: https://github.com/JosePMarques/MP2RAGE-related-scripts

But I would advise against reinventing the wheel: as far as I know all the toolbox mentioned above are straightforward implementation of this: if something does not work, better report it to the people behind the package and try to improve what’s already out there. :wink: