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