Normalize12 brick¶

Computes the deformation that best aligns the model (atlas) with an image, then if necessary, apply this deformation to spatially normalize images¶

Inputs parameters:

image_to_align <=> subj.vol [1]
The image that the template (atlas) data is warped into alignment with. Mutually exclusive with deformation_file parameter. A pathlike object or string representing an existing file (valid extensions in [.img, .nii, .hdr]).
ex. /home/username/data/raw_data/Anat.nii
deformation_file <=> subj.def [1]
File y_*.nii containing 3 deformation fields for the deformation in x, y and z dimension. Mutually exclusive with image_to_align and tpm parameters. A pathlike object or string representing an existing file (valid extensions in [.img, .nii, .hdr]).
ex. /home/username/data/downloaded_data/y_Anat.nii
apply_to_files <=> subj.resample [1]
Files to apply transformation to. They can be any images that are in register with the image used to generate the deformation. A list of items which are an existing, uncompressed file (valid extensions in [.img, .nii, .hdr]).
ex. ['/home/username/data/raw_data/Anat.nii']
jobtype
One of ‘write’ (write) or ‘est’ (estimate) or ‘estwrite’ (estimate and write).

- write: Needs the deformation_file and apply_to_files input parameters. The normalized_files output parameter comes

from the apply_to_files parameter.

- est: Needs the tpm and image_to_align input parameters. The deformation_field output parameter comes from the

image_to_align parameter.

- estwrite: Needs, at least, the tpm and image_to_align input parameters. The deformation_field and normalized_image

output parameters come from the image_to_align parameter. If the apply_to_files input parameter is defined,

the normalized_files output parameter is also created.
ex. write
bias_regularization <=> eoptions.biasreg [1]
The goal is to model, by different tissue classes, the intensity variations that arise due to different tissues, while model, with a bias field, those that occur because of the bias artifact due to the physics of MRI imaging. If the data have very little intensity non-uniformity artifact, then bias control should be increased. This effectively tells the algorithm that there is very little bias in the data, so it doesn’t try to model it (a float between 0 and 10).

- 0 No regularisation

- 0.00001 extremely light regularisation

- …

- 1 very heavy regularisation

- 10 extremely heavy regularisation
ex. 0.0001
bias_fwhm <=> eoptions.biasfwhm [1]
Full Width at Half Maximum of Gaussian smoothness of bias (a value in [30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, ‘Inf’). Smoother bias fields need fewer parameters to describe them. This means that the algorithm is faster for smoother intensity non-uniformities (e.g. 150 mm cutoff gives faster results than 30 mm cutoff).
ex. 60
tpm <=> eoptions.tpm [1]
The template in form of tissue probability atlas (a pathlike object or string representing an existing file). Mutually exclusive with the deformation_file parameter.
ex. /home/username/spm12/tpm/TPM.nii
affine_regularization_type <=> eoptions.affreg [1]
Standard space for affine registration (‘mni’ or ‘size’ or ‘none’).
ex. mni
warping_regularization <=> eoptions.reg [1]
The measure of the roughness of the deformations for registration. Involve the sum of 5 elements (list of floats).
ex. [0.0, 0.001, 0.5, 0.05, 0.2]
smoothness <=> eoptions.fwhm [1]
Value to smooth the data before normalization (a float; in mm). 0 is a good value for MRI.
ex. 0.0
sampling_distance <=> eoptions.samp [1]
Approximate distance between sampled points when estimating the model parameters (a float).
ex. 3.0
write_bounding_box <=> woptions.bb [1]
A list of 2 items which are a list of items which are a float. This is the bounding box (in mm) of the volume which is to be written (relative to the anterior commissure).
ex. [[-78, -112, -50], [78, 76, 85]]
write_voxel_sizes <=> woptions.vox [1]
A list of 3 items which are a float. This is the voxel sizes of the written normalised images.
ex. [1.0, 1.0, 1.0]
write_interp <=> woptions.interp [1]
This is the method by which the images are sampled when being written in a different space (0 <= a long integer <= 7).

- 0 Nearest neighbour

- 1 Trilinear (OK for PET, realigned fMRI, or segmentations)

- 2 2nd Degree B-spline

- …

- 7 7nd Degree B-spline.
ex. 1
out_prefix <=> woptions.prefix [1]
The prefix for the normalized outputs (a string).
ex. w

Outputs parameters

deformation_field
NIfTI file containing 3 deformation fields for the deformation in x, y and z dimension (a list of items which are a pathlike object or string representing an existing file).
ex. /home/username/data/derived_data/y_Anat.nii
normalized_files
Normalised other files (a list of items which are a pathlike object or string representing an existing file).
ex. /home/username/data/derived_data/wAnat.nii