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# interfaces.diffusion_toolkit.odf

## HARDIMat

Link to code

Wraps command hardi_mat

Use hardi_mat to calculate a reconstruction matrix from a gradient table

Inputs:

    [Mandatory]
    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
    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_info: (an existing file name)
            specify image information file. the image info file is generated
                    from original dicom image by diff_unpack program and contains image
                    orientation and other information needed for reconstruction and
                    tracking. by default will look into the image folder for .info file
    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
    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
    odf_file: (an existing file name)
            filename that contains the reconstruction points on a HEMI-sphere.
                    use the pre-set 181 points by default
    order: (an integer)
            maximum order of spherical harmonics. must be even number. default
                    is 4
    out_file: (a file name, nipype default value: recon_mat.dat)
            output matrix file
    reference_file: (an existing file name)
            provide a dicom or nifti image as the reference for the program to
                    figure out the image orientation information. if no such info was
                    found in the given image header, the next 5 options -info, etc.,
                    will be used if provided. if image orientation info can be found
                    in the given reference, all other 5 image orientation options will
                    be IGNORED

Outputs:

    out_file: (an existing file name)
            output matrix file

## ODFRecon

Link to code

Wraps command odf_recon

Use odf_recon to generate tensors and other maps

Inputs:

    [Mandatory]
    DWI: (an existing file name)
            Input raw data
    matrix: (an existing file name)
            use given file as reconstruction matrix.
    n_b0: (an integer)
            number of b0 scans. by default the program gets this information
                    from the number of directions and number of volumes in
                    the raw data. useful when dealing with incomplete raw
                    data set or only using part of raw data set to reconstruct
    n_directions: (an integer)
            Number of directions
    n_output_directions: (an integer)
            Number of output directions

    [Optional]
    args: (a string)
            Additional parameters to the command
    dsi: (a boolean)
            indicates that the data is dsi
    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
    filter: (a boolean)
            apply a filter (e.g. high pass) to the raw image
    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
    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: odf)
            Output file prefix
    output_entropy: (a boolean)
            output entropy map
    output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default value: nii)
            output file type
    sharpness: (a float)
            smooth or sharpen the raw data. factor > 0 is smoothing.
                    factor < 0 is sharpening. default value is 0
                    NOTE: this option applies to DSI study only
    subtract_background: (a boolean)
            subtract the background value before reconstruction

Outputs:

    B0: (an existing file name)
    DWI: (an existing file name)
    ODF: (an existing file name)
    entropy: (a file name)
    max: (an existing file name)

## ODFTracker

Link to code

Wraps command odf_tracker

Use odf_tracker to generate track file

Inputs:

    [Mandatory]
    ODF: (an existing file name)
    mask1_file: (a file name)
            first mask image
    max: (an existing file name)

    [Optional]
    angle_threshold: (a float)
            set angle threshold. default value is 35 degree for
                    default tracking method and 25 for rk2
    args: (a string)
            Additional parameters to the command
    disc: (a boolean)
            use disc tracking
    dsi: (a boolean)
             specify the input odf data is dsi. because dsi recon uses fixed
                    pre-calculated matrix, some special orientation patch needs to
                    be applied to keep dti/dsi/q-ball consistent.
    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
    input_data_prefix: (a string, nipype default value: odf)
            recon data prefix
    input_output_type: ('nii' or 'analyze' or 'ni1' or 'nii.gz', nipype default value: nii)
            input and output file type
    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
    limit: (an integer)
            in some special case, such as heart data, some track may go into
                    infinite circle and take long time to stop. this option allows
                    setting a limit for the longest tracking steps (voxels)
    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
    out_file: (a file name, nipype default value: tracks.trk)
            output track file
    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
    runge_kutta2: (a boolean)
            use 2nd order runge-kutta method for tracking.
                    default tracking method is non-interpolate streamline
    slice_order: (an integer)
            set the slice order. 1 means normal, -1 means reversed. default value is 1
    step_length: (a float)
            set step length, in the unit of minimum voxel size.
                    default value is 0.1.
    swap_xy: (a boolean)
            swap x and y vectors while tracking
    swap_yz: (a boolean)
            swap y and z vectors while tracking
    swap_zx: (a boolean)
            swap x and z vectors while tracking
    voxel_order: ('RAS' or 'RPS' or 'RAI' or 'RPI' or 'LAI' or 'LAS' or 'LPS' or 'LPI')
            specify the voxel order in RL/AP/IS (human brain) reference. must be
                    3 letters with no space in between.
                    for example, RAS means the voxel row is from L->R, the column
                    is from P->A and the slice order is from I->S.
                    by default voxel order is determined by the image orientation
                    (but NOT guaranteed to be correct because of various standards).
                    for example, siemens axial image is LPS, coronal image is LIP and
                    sagittal image is PIL.
                    this information also is NOT needed for tracking but will be saved
                    in the track file and is essential for track display to map onto
                    the right coordinates

Outputs:

    track_file: (an existing file name)
            output track file