cmind_apply_normalization(output_dir, ...[, ...]) normalize input_name to standard space using previously computed transforms
cmind.pipeline.cmind_apply_normalization.cmind_apply_normalization(output_dir, reg_struct, input_name, output_name='default', LRtoHR_affine=None, LRtoHR_warp=None, ANTS_path=None, Norm_Target='MNI', reg_flag='001', interp_type='', is_timeseries=False, generate_coverage=False, generate_figures=True, ForceUpdate=False, verbose=False, logger=False)[source]

normalize input_name to standard space using previously computed transforms


output_dir : str

directory in which to store the output

reg_struct : str or dict

filename of the .csv file containing the registration dictionary

input_name : str or list of str

image filename for the “moving” image

output_name : str or list of str

image filename for the output

LRtoHR_affine : str or list of str or None, optional

affine transform from functional to structural space. if LRtoHR_affine=’‘, it is assumed the image is already in structural space

LRtoHR_warp : str or list of str or None, optional

fieldmap warp correction for functional space image if LRtoHR_warp=’’ or None, no warping is applied

ANTS_path : str, optional

path to ANTs binaries

Norm_Target : {‘MNI’,’StudyTemplate’,’Structural’}, optional

Normalization target

reg_flag : str or int, optional

3 character string such as 101 controlling which registration to perform first digit is for FLIRT second digit is for FNIRT (not currently implemented) third digit is for ANTs, SyN e.g. 101 would run FLIRT & ANTs registrations. can also specify the binary “101” as the integer 5, etc.

interp_type : str or list of str, optional

{‘spline’,’bspline’} call spline based interpolation {‘lin’, ‘linear’,’trilinear’} call trilinear interpolation {‘nearestneighbour’,’nearest’,’nn’} call nearest neighbor interpolation interpolation type to use

is_timeseries : bool, optional

if true, input_name corresponds to timeseries data. the transformation will be applied to all time frames

generate_coverage : bool or list of bool, optional

if true, generate binary masks of the slice coverage. can be an array of values for each input image

generate_figures : bool,optional

if true, generate overlay images summarizing the registration

ForceUpdate : bool,optional

if True, rerun and overwrite any previously existing results

verbose : bool, optional

print additional output (to terminal and log)

logger : logging.Logger or str, optional

logging.Logger object (or string of a filename to log to)


all_output_names : list

list of all registered image filenames

See also