mct.cli_scripts package¶
Submodules¶
mct.cli_scripts.mct_calculate_noise_covariance_matrix module¶
Obtain the noise covariance matrix from noise adjustment (noiseadj) data, 2D, 3D or 4D noise volumes obtained by imaging at 0 voltage.
This is necessary if you want to do rCovSos or Roemer reconstruction in your data. The covariance matrix is a complex matrix with square size n, where n is the number of channels of the coil used to acquire your data. IT IS IMPORTANT that the order of the channels in your data IS the same order of the channels in the covariance noise matrix.
This will overwrite the output file if it exists.
mct.cli_scripts.mct_calculate_tnsr module¶
Calculate the tSNR of your timeseries.
By default this will calculate the tSNR on the last axis of the input array.
The tSNR is defined as the mean(data) / std(data).
mct.cli_scripts.mct_combine_weighted_sum module¶
Combine all the coil given some weights.
This will reconstruct the channels by summing the separate channels multiplied by the given weights.
mct.cli_scripts.mct_list_devices module¶
This script prints information about the available devices on your computer.
mct.cli_scripts.mct_list_methods module¶
List the available reconstruction methods.
This lists the methods that are available in the mct-reconstruct command.
To view more information about a method use the mct-method-info command.
mct.cli_scripts.mct_method_info module¶
Lists information about a method.
This outputs the documentation of the desired method.
mct.cli_scripts.mct_reconstruct module¶
Reconstruct your images using the desired method.
mct.cli_scripts.mct_split_volumes module¶
Split the volumes on the given axis.
Since the reconstruction method requires you to have one nifti file per channel, you need to split it if you have all your channels in one volume.
The axis are indexed zero-based. To use the last dimension use -1. By default it will split on the last dimension.