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# 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
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [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)
            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

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
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [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)
            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
    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)