interfaces.diffusion_toolkit.odf¶
HARDIMat¶
Wraps the executable command hardi_mat.
Use hardi_mat to calculate a reconstruction matrix from a gradient table
Inputs:
[Mandatory]
bvecs: (an existing file name)
b vectors file
argument: ``%s``, position: 1
bvals: (an existing file name)
b values file
[Optional]
out_file: (a file name, nipype default value: recon_mat.dat)
output matrix file
argument: ``%s``, position: 2
order: (an integer (int or long))
maximum order of spherical harmonics. must be even number. default
is 4
argument: ``-order %s``
odf_file: (an existing file name)
filename that contains the reconstruction points on a HEMI-sphere.
use the pre-set 181 points by default
argument: ``-odf %s``
reference_file: (an existing file name)
provide a dicom or nifti image as the reference for the program to
figure out the image orientation information. if no such info was
found in the given image header, the next 5 options -info, etc.,
will be used if provided. if image orientation info can be found
in the given reference, all other 5 image orientation options will
be IGNORED
argument: ``-ref %s``
image_info: (an existing file name)
specify image information file. the image info file is generated
from original dicom image by diff_unpack program and contains image
orientation and other information needed for reconstruction and
tracking. by default will look into the image folder for .info file
argument: ``-info %s``
image_orientation_vectors: (a list of from 6 to 6 items which are a
float)
specify image orientation vectors. if just one argument given,
will treat it as filename and read the orientation vectors from
the file. if 6 arguments are given, will treat them as 6 float
numbers and construct the 1st and 2nd vector and calculate the 3rd
one automatically.
this information will be used to determine image orientation,
as well as to adjust gradient vectors with oblique angle when
argument: ``-iop %f``
oblique_correction: (a boolean)
when oblique angle(s) applied, some SIEMENS dti protocols do not
adjust gradient accordingly, thus it requires adjustment for
correct
diffusion tensor calculation
argument: ``-oc``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (an existing file name)
output matrix file
ODFRecon¶
Wraps the executable command odf_recon.
Use odf_recon to generate tensors and other maps
Inputs:
[Mandatory]
DWI: (an existing file name)
Input raw data
argument: ``%s``, position: 1
n_directions: (an integer (int or long))
Number of directions
argument: ``%s``, position: 2
n_output_directions: (an integer (int or long))
Number of output directions
argument: ``%s``, position: 3
matrix: (an existing file name)
use given file as reconstruction matrix.
argument: ``-mat %s``
n_b0: (an integer (int or long))
number of b0 scans. by default the program gets this information
from the number of directions and number of volumes in
the raw data. useful when dealing with incomplete raw
data set or only using part of raw data set to reconstruct
argument: ``-b0 %s``
[Optional]
out_prefix: (a unicode string, nipype default value: odf)
Output file prefix
argument: ``%s``, position: 4
output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default
value: nii)
output file type
argument: ``-ot %s``
sharpness: (a float)
smooth or sharpen the raw data. factor > 0 is smoothing.
factor < 0 is sharpening. default value is 0
NOTE: this option applies to DSI study only
argument: ``-s %f``
filter: (a boolean)
apply a filter (e.g. high pass) to the raw image
argument: ``-f``
subtract_background: (a boolean)
subtract the background value before reconstruction
argument: ``-bg``
dsi: (a boolean)
indicates that the data is dsi
argument: ``-dsi``
output_entropy: (a boolean)
output entropy map
argument: ``-oe``
image_orientation_vectors: (a list of from 6 to 6 items which are a
float)
specify image orientation vectors. if just one argument given,
will treat it as filename and read the orientation vectors from
the file. if 6 arguments are given, will treat them as 6 float
numbers and construct the 1st and 2nd vector and calculate the 3rd
one automatically.
this information will be used to determine image orientation,
as well as to adjust gradient vectors with oblique angle when
argument: ``-iop %f``
oblique_correction: (a boolean)
when oblique angle(s) applied, some SIEMENS dti protocols do not
adjust gradient accordingly, thus it requires adjustment for
correct
diffusion tensor calculation
argument: ``-oc``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
B0: (an existing file name)
DWI: (an existing file name)
max: (an existing file name)
ODF: (an existing file name)
entropy: (a file name)
ODFTracker¶
Wraps the executable command odf_tracker.
Use odf_tracker to generate track file
Inputs:
[Mandatory]
max: (an existing file name)
ODF: (an existing file name)
mask1_file: (a file name)
first mask image
argument: ``-m %s``, position: 2
[Optional]
input_data_prefix: (a unicode string, nipype default value: odf)
recon data prefix
argument: ``%s``, position: 0
out_file: (a file name, nipype default value: tracks.trk)
output track file
argument: ``%s``, position: 1
input_output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype
default value: nii)
input and output file type
argument: ``-it %s``
runge_kutta2: (a boolean)
use 2nd order runge-kutta method for tracking.
default tracking method is non-interpolate streamline
argument: ``-rk2``
step_length: (a float)
set step length, in the unit of minimum voxel size.
default value is 0.1.
argument: ``-l %f``
angle_threshold: (a float)
set angle threshold. default value is 35 degree for
default tracking method and 25 for rk2
argument: ``-at %f``
random_seed: (an integer (int or long))
use random location in a voxel instead of the center of the voxel
to seed. can also define number of seed per voxel. default is 1
argument: ``-rseed %s``
invert_x: (a boolean)
invert x component of the vector
argument: ``-ix``
invert_y: (a boolean)
invert y component of the vector
argument: ``-iy``
invert_z: (a boolean)
invert z component of the vector
argument: ``-iz``
swap_xy: (a boolean)
swap x and y vectors while tracking
argument: ``-sxy``
swap_yz: (a boolean)
swap y and z vectors while tracking
argument: ``-syz``
swap_zx: (a boolean)
swap x and z vectors while tracking
argument: ``-szx``
disc: (a boolean)
use disc tracking
argument: ``-disc``
mask1_threshold: (a float)
threshold value for the first mask image, if not given, the program
will try automatically find the threshold
mask2_file: (a file name)
second mask image
argument: ``-m2 %s``, position: 4
mask2_threshold: (a float)
threshold value for the second mask image, if not given, the program
will try automatically find the threshold
limit: (an integer (int or long))
in some special case, such as heart data, some track may go into
infinite circle and take long time to stop. this option allows
setting a limit for the longest tracking steps (voxels)
argument: ``-limit %d``
dsi: (a boolean)
specify the input odf data is dsi. because dsi recon uses fixed
pre-calculated matrix, some special orientation patch needs to
be applied to keep dti/dsi/q-ball consistent.
argument: ``-dsi``
image_orientation_vectors: (a list of from 6 to 6 items which are a
float)
specify image orientation vectors. if just one argument given,
will treat it as filename and read the orientation vectors from
the file. if 6 arguments are given, will treat them as 6 float
numbers and construct the 1st and 2nd vector and calculate the 3rd
one automatically.
this information will be used to determine image orientation,
as well as to adjust gradient vectors with oblique angle when
argument: ``-iop %f``
slice_order: (an integer (int or long))
set the slice order. 1 means normal, -1 means reversed. default
value is 1
argument: ``-sorder %d``
voxel_order: ('RAS' or 'RPS' or 'RAI' or 'RPI' or 'LAI' or 'LAS' or
'LPS' or 'LPI')
specify the voxel order in RL/AP/IS (human brain) reference. must be
3 letters with no space in between.
for example, RAS means the voxel row is from L->R, the column
is from P->A and the slice order is from I->S.
by default voxel order is determined by the image orientation
(but NOT guaranteed to be correct because of various standards).
for example, siemens axial image is LPS, coronal image is LIP and
sagittal image is PIL.
this information also is NOT needed for tracking but will be saved
in the track file and is essential for track display to map onto
the right coordinates
argument: ``-vorder %s``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
track_file: (an existing file name)
output track file