Hi Stefanie,
There are several options that might improve the results.
1) changing regularisation parameters
It might be a good idea to change “-nl_update_smooth” or “-nl_disp_smooth” from default. That would (although might lead to negative Jacobians).
E.g., -nl_update_smooth 1.00 -nl_disp_smooth 0.75.
2) addition of cortex masks
In my experience - adding binary cortex masks as additional channels significantly improves alignment for the cortex (or even GM probability priors).
3) using NCC metric non-linear registrastration part
The current version of registration in mrtrix in the main branch uses only SSD metric.
This is one of the reasons why it might be optimal for structural registration (e.g., intensities are not normalised / differences in the acquisition parameters / lesions with extreme intensity difference).
We’ve been working on adding the NCC metric to registration / population_template function and you can install it from “mrreg_lite2” branch from:
You can run it with the following options (global NCC for linear with “0” radius and local NCC with “3” voxel radius):
-linear_metric ncc -linear_metric.radius 0
-nl_metric ncc -nl_metric.radius 3
Please note that in linear registration guided by NCC is not symmetric (it is symmetric for nonlinear NCC registration).
So in summary - it might be a good idea to use SSD for the linear phases of population_template and local NCC for nonlinear + decrease regularisation parameters and add cortex masks.
Hope this will help!
Let me know if there will be any issues.
Best,
Alena