This functionality is only available in the development version of FreeSurfer.

Author: Juan Eugenio Iglesias

E-mail: e.iglesias [at]

Rather than directly contacting the author, please post your questions on this module to the FreeSurfer mailing list at freesurfer [at]

If you use this tool in your analysis, please cite:

Since the robustness against MR contrast / orientation /resolution with a single model was not presented in the original SynthSR paper, we would also greatly appreciate it if you could please also cite:


  1. Motivation and General Description
  2. Usage
  3. Processing Hyperfine scans

1. Motivation and General Description

This tool implements SynthSR, a convolutional neural network that turns a clinical MRI scan (or even CT scan!) of any orientation, resolution and contrast into 1 mm isotropic MP-RAGE. You can then run your favorite neuroimaging software (including FreeSurfer, of course!) on these synthetic images for segmentation / registration / any other analysis.


2. Usage

We provide an "all purpose" model that can be applied to a scan of any resolution of contrast. Once FreeSurfer has been sourced, you can simply test SynthSR on your own data with:

mri_SynthSR --i <input> --o <output> [--threads <n_threads>] [--ct]


The synthetic 1mm MP-RAGE will be of a standard contrast, bias field corrected, and with white matter lesions inpainted.
Regarding CT scans: SynthSR does a decent job with CT them! The only caveat is that the dynamic range of CT is very different to that of MRI, so they need to be clipped to [0, 80] Hounsfield units. You can use the --ct flag to do this, as long as your image volume is in Hounsfield units. If not, you will have to clip to the Hounsfield equivalent yourself (and not use --ct).

3. Processing Hyperfine scans

We also provide a dedicated, multispectral (T1+T2) model for Hyperfine scans. While you can apply the all-purpose model above to the T1 or T2 scans from Hyperfine, their relatively low in-plane resolution (1.5mm) and particularly high noise often degrades the results (especially when processing the T1). Beter results can be obtained by using both the T1 (FSE) and T2 (FSE) scans as input to a separate, dedicated version of SynthSR:

mri_SynthSR_hyperfine  --t1 <input_t1> --t2 <input_t2> --o <output>  [--threads <n_threads>]

where, as in the previous version, <input_t1>, <input_t2> and <output> can be single files or directories.


We emphasize that:

If there is motion between the T1 and T2 scans, the T2 needs to be pre-registered to the space of the T1, but without resamling to the 1.5x1.5x5mm space of the T1, which would introduce large resampling artifacts. This can be done with FreeSurfer's mri_robust_register:

mri_robust_register --mov T2.nii.gz --dst T1.nii.gz --mapmovhdr T2.reg.nii.gz --cost NMI --noinit --nomulti --lta /dev/null

SynthSR (last edited 2021-08-03 12:10:17 by JuanIglesias)