interfaces.diffusion_toolkit.dti

DTIRecon

Link to code

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 unicode 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 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
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 unicode 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

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

Link to code

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 unicode string)
        Additional parameters to the command
        flag: %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
input_data_prefix: (a unicode 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 %d
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
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)