Inheritance diagram for nipy.algorithms.registration.groupwise_registration:
Bases: nipy.algorithms.registration.groupwise_registration.Realign4d
Return a list of 4d nibabel-like images corresponding to the resampled runs.
Bases: object
Class to represent a sequence of 3d scans (possibly acquired on a slice-by-slice basis).
Methods
grid_time | |
z_to_slice |
Configure fMRI acquisition time parameters.
tr_slices : inter-slice repetition time, same as tr for slices start : starting acquisition time respective to the implicit
time origin
slice_order : string or array
tv = grid_time(zv, t) zv, tv are grid coordinates; t is an actual time value.
Account for the fact that slices may be stored in reverse order wrt the scanner coordinate system convention (slice 0 == bottom of the head)
Bases: object
Return a list of 4d nibabel-like images corresponding to the resampled runs.
Bases: object
The idea is to compute the global variance using the following decomposition:
with alpha=(n-1)/n V1, beta = (n-1)/n^2, d2 = (x1-m1)^2.
Only the second term is variable when one image moves while all other images are fixed.
Mean square intensity difference
x,y,z,t are “ideal grid” coordinates X,Y,Z,T are “acquisition grid” coordinates
No need to invoke self.init_motion_detection.
Parameters : | runs : list of Image4d objects |
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Returns : | transforms : list
transforms map an ‘ideal’ 4d grid (conventionally aligned with the : first scan of the first run) to the ‘acquisition’ 4d grid for each : run : |
Parameters : | im4d : Image4d instance |
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