cmind.utils.fsl_mcflirt_opt

mcflirt motion correction utility

Functions

fsl_mcflirt_opt(input_name[, cost_type, ...]) Utility that tries to improve the robustness of mcflirt by running in 3 stages:
cmind.utils.fsl_mcflirt_opt.fsl_mcflirt_opt(input_name, cost_type='normcorr', out_name='', output_dir=None, verbose=False, logger=None)[source]

Utility that tries to improve the robustness of mcflirt by running in 3 stages:

Parameters:

input_name : str

4D NIFTI or NIFTI-GZ volume to motion correct

cost_type : {‘normcorr’,’mutualinfo’,’woods’,’corratio’,’normmi’,’leastsquares’}, optional

cost function used by mcflirt (default = ‘normcorr’)

out_name : str, optional

output filename to be used for the motion-corrected timeseries

output_dir : str, optional

if None, output will be stored in current working directory via os.getcwd()

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)

Returns:

out_name : str

output filename of the motion-corrected timeseries

Notes

The mcflirt stages run are: 1.) Fast initial registration to an initial frame without sinc interpolation to find the frame closest to the mean position 2.) Rerun mcflirt using this “optimal” reference frame and with sinc interpolation to reduce blurring 3.) Take a timeseries average of registered frames from stage two and run mcflirt a 3rd time using this high SNR timeseries average as the reference mcflirt will duplicate the top and bottom slice during the calculations to allow some corrections to be made to the edge slices

cost_type options are: normcorr (default), mutualinfo, woods, corratio, normmi, leastsquares