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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
            flag: %s, position: 1

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
            flag: -info %s
    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
    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
    odf_file: (an existing file name)
            filename that contains the reconstruction points on a HEMI-sphere.
             use the pre-set 181 points by default
            flag: -odf %s
    order: (an integer (int or long))
            maximum order of spherical harmonics. must be even number. default
             is 4
            flag: -order %s
    out_file: (a file name, nipype default value: recon_mat.dat)
            output matrix file
            flag: %s, position: 2
    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
            flag: -ref %s
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'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:

    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
            flag: %s, position: 1
    matrix: (an existing file name)
            use given file as reconstruction matrix.
            flag: -mat %s
    n_b0: (an integer (int or long))
            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
            flag: -b0 %s
    n_directions: (an integer (int or long))
            Number of directions
            flag: %s, position: 2
    n_output_directions: (an integer (int or long))
            Number of output directions
            flag: %s, position: 3

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    dsi: (a boolean)
            indicates that the data is dsi
            flag: -dsi
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    filter: (a boolean)
            apply a filter (e.g. high pass) to the raw image
            flag: -f
    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
    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: odf)
            Output file prefix
            flag: %s, position: 4
    output_entropy: (a boolean)
            output entropy map
            flag: -oe
    output_type: (u'nii' or u'analyze' or u'ni1' or u'nii.gz', nipype
             default value: nii)
            output file type
            flag: -ot %s
    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
            flag: -s %f
    subtract_background: (a boolean)
            subtract the background value before reconstruction
            flag: -bg
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'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:

    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
            flag: -m %s, position: 2
    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
            flag: -at %f
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    disc: (a boolean)
            use disc tracking
            flag: -disc
    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.
            flag: -dsi
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, 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
    input_data_prefix: (a unicode string, nipype default value: odf)
            recon data prefix
            flag: %s, position: 0
    input_output_type: (u'nii' or u'analyze' or u'ni1' or u'nii.gz',
             nipype default value: nii)
            input and output file type
            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
    limit: (an integer (int or long))
            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)
            flag: -limit %d
    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
    out_file: (a file name, nipype default value: tracks.trk)
            output track file
            flag: %s, position: 1
    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 %s
    runge_kutta2: (a boolean)
            use 2nd order runge-kutta method for tracking.
             default tracking method is non-interpolate streamline
            flag: -rk2
    slice_order: (an integer (int or long))
            set the slice order. 1 means normal, -1 means reversed. default
            value is 1
            flag: -sorder %d
    step_length: (a float)
            set step length, in the unit of minimum voxel size.
             default value is 0.1.
            flag: -l %f
    swap_xy: (a boolean)
            swap x and y vectors while tracking
            flag: -sxy
    swap_yz: (a boolean)
            swap y and z vectors while tracking
            flag: -syz
    swap_zx: (a boolean)
            swap x and z vectors while tracking
            flag: -szx
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'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
    voxel_order: (u'RAS' or u'RPS' or u'RAI' or u'RPI' or u'LAI' or
             u'LAS' or u'LPS' or u'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
            flag: -vorder %s

Outputs:

    track_file: (an existing file name)
            output track file