interfaces.diffusion_toolkit.dti¶
DTIRecon¶
Wraps command dti_recon
Use dti_recon to generate tensors and other maps
Inputs:
[Mandatory]
DWI: (an existing file name)
Input diffusion volume
flag: %s, position: 1
bvals: (an existing file name)
b values file
bvecs: (an existing file name)
b vectors file
flag: -gm %s
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
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.
flag: -b0_th
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
flag: -iop %f
n_averages: (an integer (int or long))
Number of averages
flag: -nex %s
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
flag: -oc
out_prefix: (a string, nipype default value: dti)
Output file prefix
flag: %s, position: 2
output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default
value: nii)
output file type
flag: -ot %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
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)
DTITracker¶
Wraps command dti_tracker
Inputs:
[Mandatory]
mask1_file: (a file name)
first mask image
flag: -m %s, position: 2
[Optional]
angle_threshold: (a float)
set angle threshold. default value is 35 degree
flag: -at %f
angle_threshold_weight: (a float)
set angle threshold weighting factor. weighting will be be applied
on top of the angle_threshold
flag: -atw %f
args: (a string)
Additional parameters to the command
flag: %s
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
flag: %s, position: 0
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'
flag: -it %s
invert_x: (a boolean)
invert x component of the vector
flag: -ix
invert_y: (a boolean)
invert y component of the vector
flag: -iy
invert_z: (a boolean)
invert z component of the vector
flag: -iz
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
flag: -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
output_file: (a file name, nipype default value: tracks.trk)
flag: %s, position: 1
output_mask: (a file name)
output a binary mask file in analyze format
flag: -om %s
primary_vector: ('v2' or 'v3')
which vector to use for fibre tracking: v2 or v3. If not set use v1
flag: -%s
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
flag: -rseed
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
flag: -l %f
swap_xy: (a boolean)
swap x & y vectors while tracking
flag: -sxy
swap_yz: (a boolean)
swap y & z vectors while tracking
flag: -syz
swap_zx: (a boolean)
swap x & z vectors while tracking
flag: -szx
tensor_file: (an existing file name)
reconstructed tensor file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
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
flag: -%s
Outputs:
mask_file: (an existing file name)
track_file: (an existing file name)