Note
This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
nilearn.masking.compute_multi_epi_mask#
- nilearn.masking.compute_multi_epi_mask(epi_imgs, lower_cutoff=0.2, upper_cutoff=0.85, connected=True, opening=2, threshold=0.5, target_affine=None, target_shape=None, exclude_zeros=False, n_jobs=1, memory=None, verbose=0)[source]#
Compute a common mask for several sessions or subjects of fMRI data.
Uses the mask-finding algorithms to extract masks for each session or subject, and then keep only the main connected component of the a given fraction of the intersection of all the masks.
- Parameters
- epi_imgs
list
of Niimg-like objects See https://nilearn.github.io/stable/manipulating_images/input_output.html # noqa:E501 A list of arrays, each item being a subject or a session. 3D and 4D images are accepted.
Note
If 3D images are given, we suggest to use the mean image of each session.
- threshold
float
, optional The inter-session threshold: the fraction of the total number of sessions in for which a voxel must be in the mask to be kept in the common mask. threshold=1 corresponds to keeping the intersection of all masks, whereas threshold=0 is the union of all masks.
- lower_cutoff
float
, optional Lower fraction of the histogram to be discarded. Default=0.2.
- upper_cutoff
float
, optional Upper fraction of the histogram to be discarded. Default=0.85.
- connected
bool
, optional If connected is True, only the largest connect component is kept. Default=True.
- exclude_zeros
bool
, optional Consider zeros as missing values for the computation of the threshold. This option is useful if the images have been resliced with a large padding of zeros. Default=False.
- target_affine
numpy.ndarray
, optional. If specified, the image is resampled corresponding to this new affine.
target_affine
can be a 3x3 or a 4x4 matrix. Default=None.Note
This parameter is passed to
nilearn.image.resample_img
.- target_shape
tuple
orlist
, optional. If specified, the image will be resized to match this new shape.
len(target_shape)
must be equal to 3.Note
If
target_shape
is specified, atarget_affine
of shape(4, 4)
must also be given.Default=None.
Note
This parameter is passed to
nilearn.image.resample_img
.- memoryinstance of
joblib.Memory
orstr
Used to cache the masking process. By default, no caching is done. If a
str
is given, it is the path to the caching directory.- n_jobs
int
, optional. The number of CPUs to use to do the computation. -1 means ‘all CPUs’. Default=1.
- epi_imgs
- Returns
- mask3D
nibabel.nifti1.Nifti1Image
The brain mask.
- mask3D