nireports.reportlets.modality.dwi module¶

Visualizations for diffusion MRI data.

nireports.reportlets.modality.dwi.draw_circles(positions, radius, n_samples=20)[source]¶

Draw circular patches (lying on a sphere) at given positions.

List of changes

Modified to take the full list of normalized bvecs and corresponding circle radii instead of taking the list of bvecs and radii for a specific shell (b-value).

Parameters:

positions (numpy.ndarray) – An array \(N \times 3\) of 3D cartesian positions.
radius (float) – The reference radius (or, the radius in single-shell plots)
n_samples (int) – The number of samples on the sphere.

Returns:

circles – Circular patches.

Return type:

numpy.ndarray

nireports.reportlets.modality.dwi.draw_points(gradients, ax, rad_min=0.3, rad_max=0.7, colormap='viridis')[source]¶

Draw the vectors on a shell.

List of changes

The input is a single 2D numpy array of the gradient table in RAS+B format
The scaling of the circle radius for each bvec proportional to the inverse of the bvals. A minimum/maximal value for the radii can be specified.
Circles for each bvec are drawn at once instead of looping over the shells.
Some variables have been renamed (like vects to bvecs)

Parameters:

gradients (numpy.ndarray) – An (N, 4) shaped array of the gradient table in RAS+B format.
ax (matplotlib.axes.Axis) – The matplotlib axes instance to plot in.
rad_min (float between 0 and 1) – Minimum radius of the circle that renders a gradient direction.
rad_max (float between 0 and 1) – Maximum radius of the circle that renders a gradient direction.
colormap (str) – matplotlib colormap name.

nireports.reportlets.modality.dwi.get_segment_labels(filepath, keywords, delimiter=' ', index_position=0, label_position=1)[source]¶

Get segment labels from file by keyword for get_segments function

Parameters:

filepath (string) – Path to segment label text file, such as freesurfer label file
keywords (list of string) – List of label keywords. All labels containing the keyword will be grouped together. e.g. [“Cerebral_White_Matter”, “Cerebral_Cortex”, “Ventricle”]
delimiter (string, optional) – Delimiter between label index and label string in label file (’ ‘ for freesurfer label file)
index_position (int, optional) – Position of label index in label file (0 for freesurfer label file)
label_position (int, optional) – Position of label string in label file (1 for freesurfer label file)

Returns:

dict
e.g. {‘Cerebral_White_Matter’ ([2, 41],) – ‘Cerebral_Cortex’: [3, 42], ‘Ventricle’: [4, 14, 15, 43, 72]}

nireports.reportlets.modality.dwi.get_segments(segment_mask, segment_labels)[source]¶

Return segments dict for plot_carpet function

Parameters:

segment_mask (numpy.ndarray) – Segmentation mask of DW imaging data
segment_labels (dict) – Dictionary of segment labels e.g. {‘Cerebral_White_Matter’: [2, 41],

‘Cerebral_Cortex’: [3, 42], ‘Ventricle’: [4, 14, 15, 43, 72]}

Returns:

segments – A mapping between segment labels (e.g., “Left Cortex”) and list of indexes in the data array.

Return type:

dict

nireports.reportlets.modality.dwi.nii_to_carpetplot_data(nii, bvals=None, divide_by_b0=True, drop_b0=True, sort_by_bval=False, mask_nii=None, segment_labels=None)[source]¶

Convert nii to data matrix for carpet plot

Parameters:

nii (Nifti1Image) – DW imaging data
bvals (numpy.ndarray, optional) – Rounded bvals
divide_by_b0 (bool, optional) – Divide data by mean b0
drop_b0 (bool, optional) – Flag to drop b0 data
sort_by_bval (bool, optional) – Flag to reorder time points by bvalue
mask_nii (Nifti1Image, optional) – Boolean or segmentation mask of DW imaging data
segment_labels (dict, optional) – Dictionary of segment labels e.g. {‘Cerebral_White_Matter’: [2, 41],

‘Cerebral_Cortex’: [3, 42], ‘Ventricle’: [4, 14, 15, 43, 72]}

Returns:

data (N x T numpy.array) – The functional data to be plotted (N sampling locations by T timepoints).
segments (dict) – A mapping between segment labels (e.g., “Left Cortex”) and list of indexes in the data array.

nireports.reportlets.modality.dwi.plot_dwi(dataobj, affine, gradient=None, **kwargs)[source]¶

Plot orthogonal (axial, coronal, sagittal) slices of a given DWI volume. The slices displayed are determined by a tuple contained in the cut_coords keyword argument.

Parameters:

dataobj (numpy.ndarray) – DWI volume data: a single 3D volume from a given gradient direction.
affine (numpy.ndarray) – Affine transformation matrix.
gradient (numpy.ndarray) – Gradient values in RAS+b format at the chosen gradient direction.
kwargs (dict) – Extra args given to nilearn.plotting.plot_anat().

Returns:

An instance of the OrthoSlicer class. If output_file is defined, None is returned.

Return type:

nilearn.plotting.displays.OrthoSlicer or None

nireports.reportlets.modality.dwi.plot_gradients(gradients, title=None, ax=None, spacing=0.05, **kwargs)[source]¶

Draw the vectors on a unit sphere using a different color (as given by the colormap property in the extra keywowrd arguments) for each b-value.