Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/diffusion_toolkit/dti.py#L57
Wraps command dti_recon
Use dti_recon to generate tensors and other maps
Inputs:
[Mandatory]
DWI: (an existing file name)
Input diffusion volume
bvals: (an existing file name)
b values file
bvecs: (an existing file name)
b vectors file
[Optional]
args: (a string)
Additional parameters to the command
b0_threshold: (a float)
program will use b0 image with the given threshold to mask out high
background of fa/adc maps. by default it will calculate threshold
automatically. but if it failed, you need to set it manually.
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
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
n_averages: (an integer)
Number of averages
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
out_prefix: (a string, nipype default value: dti)
Output file prefix
output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default value: nii)
output file type
Outputs:
ADC: (an existing file name)
B0: (an existing file name)
FA: (an existing file name)
FA_color: (an existing file name)
L1: (an existing file name)
L2: (an existing file name)
L3: (an existing file name)
V1: (an existing file name)
V2: (an existing file name)
V3: (an existing file name)
exp: (an existing file name)
tensor: (an existing file name)
Code: file:///build/buildd/nipype-0.5.3/nipype/interfaces/diffusion_toolkit/dti.py#L147
Wraps command dti_tracker
Inputs:
[Mandatory]
mask1_file: (a file name)
first mask image
[Optional]
angle_threshold: (a float)
set angle threshold. default value is 35 degree
angle_threshold_weight: (a float)
set angle threshold weighting factor. weighting will be be applied on top of the
angle_threshold
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
input_data_prefix: (a string, nipype default value: dti)
for internal naming use only
input_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz')
input and output file type. accepted values are:
analyze -> analyze format 7.5
ni1 -> nifti format saved in seperate .hdr and .img file
nii -> nifti format with one .nii file
nii.gz -> nifti format with compression
default type is 'nii'
invert_x: (a boolean)
invert x component of the vector
invert_y: (a boolean)
invert y component of the vector
invert_z: (a boolean)
invert z component of the vector
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
mask2_threshold: (a float)
threshold value for the second mask image, if not given, the program will try
automatically find the threshold
output_file: (a file name, nipype default value: tracks.trk)
output_mask: (a file name)
output a binary mask file in analyze format
primary_vector: ('v2' or 'v3')
which vector to use for fibre tracking: v2 or v3. If not set use v1
random_seed: (an integer)
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
step_length: (a float)
set step length, in the unit of minimum voxel size.
default value is 0.5 for interpolated streamline method
and 0.1 for other methods
swap_xy: (a boolean)
swap x & y vectors while tracking
swap_yz: (a boolean)
swap y & z vectors while tracking
swap_zx: (a boolean)
swap x & z vectors while tracking
tensor_file: (an existing file name)
reconstructed tensor file
tracking_method: ('fact' or 'rk2' or 'tl' or 'sl')
fact -> use FACT method for tracking. this is the default method.
rk2 -> use 2nd order runge-kutta method for tracking.
tl -> use tensorline method for tracking.
sl -> use interpolated streamline method with fixed step-length
Outputs:
mask_file: (an existing file name)
track_file: (an existing file name)