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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
    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)

## DTITracker

Link to code

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)