Dear all,
In the present I’ve used my own nipype scripts to do some preprocessing of (rs)fMRI data.
However, fmriprep has taken the preprocessing to a higher level with the scripts in combination
with BIDS compatible data-sets. The problem I’m currently facing is that the registration
of EPI to T1w data fails (fmriprep.workflows.epi.init_epi_reg_wf). The FOV of the EPIs is rather small (32 slices) and previously I used FLIRT with 3 DOF or with the simple3D.sch schedule to get a proper (initial) registration.
More recently I’ve done the same with ANTs with the following code:
# coregistration step based on rigid transformation using ANTs
# see e.g., # see https://github.com/binarybottle/mindboggle/issues/15
coreg = pe.Node(Registration(), name='CoregAnts')
coreg.inputs.output_warped_image = 'func2highres_.nii.gz'
coreg.inputs.output_transform_prefix = "func2highres_"
coreg.inputs.transforms = ['Rigid']
coreg.inputs.transform_parameters = [(0.1,), (0.1,)]
coreg.inputs.number_of_iterations = [[1000,500,250,100]]
coreg.inputs.dimension = 3
coreg.inputs.write_composite_transform = True
coreg.inputs.collapse_output_transforms = True
coreg.inputs.metric = ['MI']
coreg.inputs.metric_weight = [1]
coreg.inputs.radius_or_number_of_bins = [32]
coreg.inputs.sampling_strategy = ['Regular']
coreg.inputs.sampling_percentage = [0.25]
coreg.inputs.convergence_threshold = [1.e-8]
coreg.inputs.convergence_window_size = [10]
coreg.inputs.smoothing_sigmas = [[3,2,1,0]]
coreg.inputs.sigma_units = ['mm']
coreg.inputs.shrink_factors = [[8,4,2,1]]
coreg.inputs.use_estimate_learning_rate_once = [True]
coreg.inputs.use_histogram_matching = [False]
coreg.inputs.initial_moving_transform_com = True
coreg.inputs.output_warped_image = True
coreg.inputs.winsorize_lower_quantile = 0.01
coreg.inputs.winsorize_upper_quantile = 0.99
Would it be possible (i.e. feasible) to include either the pure FLIRT or ANTs solution in fmriprep as well? I’ve noticed that FLIRT is actually used to initialize the registration, but with 6 DOF (https://github.com/poldracklab/fmriprep/blob/master/fmriprep/workflows/util.py, line 169)
I’d like to have the option to use less than 6, or to limit the variation in the 6 DOF (esp. rotation, the data seems rotated over more than 90 degrees). This would be something to add to the [--bold2t1w-dof {6,9,12}] option. Any pointers, ideas, takers?
Cheers,
Cris