.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.camino.odf
=====================


.. _nipype.interfaces.camino.odf.LinRecon:


.. index:: LinRecon

LinRecon
--------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/camino/odf.py#L111>`__

Wraps command **linrecon**

Runs a linear transformation in each voxel.

Reads  a  linear  transformation from the matrix file assuming the
imaging scheme specified in the scheme file. Performs the linear
transformation on the data in every voxel and outputs the result to
the standard output. The ouput in every voxel is actually:
    [exit code, ln(S(0)), p1, ..., pR]
where p1, ..., pR are the parameters of the reconstruction.
Possible exit codes are:
   0. No problems.
   6. Bad data replaced by substitution of zero.
The matrix must be R by N+M where N+M is the number of measurements
and R is the number of parameters of the reconstruction. The matrix
file contains binary double-precision floats. The matrix elements
are stored row by row.

Example
~~~~~~~~~
First run QBallMX and create a linear transform matrix using
Spherical Harmonics (sh).

>>> import nipype.interfaces.camino as cam
>>> qballmx = cam.QBallMX()
>>> qballmx.inputs.scheme_file = 'A.scheme'
>>> qballmx.inputs.basistype = 'sh'
>>> qballmx.inputs.order = 4
>>> qballmx.run()            # doctest: +SKIP

Then run it over each voxel using LinRecon

>>> qballcoeffs = cam.LinRecon()
>>> qballcoeffs.inputs.in_file = 'SubjectA.Bfloat'
>>> qballcoeffs.inputs.scheme_file = 'A.scheme'
>>> qballcoeffs.inputs.qball_mat = 'A_qmat.Bdouble'
>>> qballcoeffs.inputs.normalize = True
>>> qballcoeffs.run()         # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                voxel-order data filename
                flag: %s, position: 1
        qball_mat: (an existing file name)
                Linear transformation matrix.
                flag: %s, position: 3
        scheme_file: (an existing file name)
                Specifies the scheme file for the diffusion MRI data
                flag: %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]
        args: (a string)
                Additional parameters to the command
                flag: %s
        bgmask: (an existing file name)
                background mask
                flag: -bgmask %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
        log: (a boolean)
                Transform the log measurements rather than the measurements
                themselves
                flag: -log
        normalize: (a boolean)
                Normalize the measurements and discard the zero measurements before
                the linear transform.
                flag: -normalize
        out_file: (a file name)
                flag: > %s, position: -1

Outputs::

        recon_data: (an existing file name)
                Transformed data

.. _nipype.interfaces.camino.odf.MESD:


.. index:: MESD

MESD
----

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/camino/odf.py#L194>`__

Wraps command **mesd**

MESD is a general program for maximum entropy spherical deconvolution.
It also runs PASMRI, which is a special case of spherical deconvolution.
The input data must be in voxel order.

The format of the output in each voxel is:
{ exitcode, ln(A^star(0)), lambda_0, lambda_1, ..., lambda_N }

The  exitcode  contains  the  results of three tests. The first test thresholds
the maximum relative error between the numerical integrals computed at con-
vergence and those computed using a larger test point set; if the error is
greater than a threshold the exitcode is increased from zero to one as a
warning; if it is greater than a larger threshold the exitcode is increased to
two to suggest failure. The  second  test  thresholds  the predicted  error in
numerical integrals computed using the test point set; if the predicted error
is greater than a threshold the exitcode is increased by 10. The third test
thresholds the RMS error between the measurements and their predictions from
the fitted deconvolution; if the errors are greater than a threshold, the exit
code is increased by 100. An exitcode of 112 means that all three tests were
failed and the result is likely to be unreliable.  If all is well the exitcode
is zero. Results are often still reliable even if one or two of the tests are
failed.

Other possible exitcodes are:
 5   - The optimization failed to converge
-1   - Background
-100 - Something wrong in the MRI data, e.g. negative or zero measurements,
       so that the optimization could not run.

The  standard  MESD  implementation  is computationally demanding, particularly
as the number of measurements increases (computation is approximately O(N^2),
where N is the number of measurements). There are two ways to obtain significant
computational speed-up:

i) Turn off error checks and use a small point set for computing numerical
integrals in the algorithm by adding the flag -fastmesd. Sakaie CDMRI 2008
shows that using the smallest point set  (-basepointset  0)  with  no
error  checks  usually  has only a minor effect on the output of the algorithm,
but provides a major reduction in computation time. You can increase the point
set size using -basepointset with an argument higher than 0, which may produce
better results in some voxels, but will increase computation time, which
approximately doubles every time the point set index increases by 1.

ii) Reduce the complexity of the maximum entropy encoding using -mepointset <X>.
By default <X> = N, the number of measurements, and is the number of parameters
in the max.  ent. representation of the  output  function, ie  the  number of
lambda parameters, as described in Jansons and Alexander Inverse Problems 2003.
However, we can represent the function using less components and <X> here
specifies the number of lambda parameters. To obtain speed-up, set <X>
< N; complexity become O(<X>^2) rather than O(N^2). Note that <X> must be chosen
so that the camino/PointSets directory contains a point set  with  that  number
of  elements.  When  -mepointset decreases, the  numerical  integration checks
make less and less of a difference and smaller point sets for numerical
integration (see -basepointset) become adequate. So when <X> is low -fastmesd is
worth using to get even more speed-up.

The choice of <X> is a parameter of the technique. Too low and you lose angular
resoloution; too high and you see no computational benefit and may even suffer
from overfitting. Empirically, we  have  found  that  <X>=16 often  gives  good
results and good speed up, but it is worth trying a few values a comparing
performance. The reduced encoding is described in the following ISMRM abstract:
Sweet and Alexander "Reduced Encoding Persistent Angular Structure" 572 ISMRM 2010.

Example
~~~~~~~~~
Run MESD on every voxel of the data file SubjectA.Bfloat using the PASMRI kernel.

>>> import nipype.interfaces.camino as cam
>>> mesd = cam.MESD()
>>> mesd.inputs.in_file = 'SubjectA.Bfloat'
>>> mesd.inputs.scheme_file = 'A.scheme'
>>> mesd.inputs.inverter = 'PAS'
>>> mesd.inputs.inverter_param = 1.4
>>> mesd.run()            # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                voxel-order data filename
                flag: -inputfile %s, position: 1
        inverter: ('SPIKE' or 'PAS')
                The inversion index specifies the type of inversion to perform on
                the data.The currently available choices are:Inverter name |
                Inverter parameters---------------|------------------SPIKE | bd
                (b-value x diffusivity along the fibre.)PAS | r
                flag: -filter %s, position: 2
        inverter_param: (a float)
                Parameter associated with the inverter. Cf. inverter description
                formore information.
                flag: %f, position: 3
        scheme_file: (an existing file name)
                Specifies the scheme file for the diffusion MRI data
                flag: -schemefile %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
        bgmask: (an existing file name)
                background mask
                flag: -bgmask %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
        fastmesd: (a boolean)
                Turns off numerical integration checks and fixes the integration
                point set size at that ofthe index specified by -basepointset..
                flag: -fastmesd
                requires: mepointset
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        inputdatatype: ('float' or 'char' or 'short' or 'int' or 'long' or
                 'double')
                Specifies the data type of the input file: "char", "short", "int",
                "long","float" or "double". The input file must have BIG-ENDIAN
                ordering.By default, the input type is "float".
                flag: -inputdatatype %s
        mepointset: (an integer)
                Use a set of directions other than those in the scheme file for the
                deconvolution kernel.The number refers to the number of directions
                on the unit sphere. For example, "-mepointset 54" uses the
                directions in "camino/PointSets/Elec054.txt".
                flag: -mepointset %d
        out_file: (a file name)
                flag: > %s, position: -1

Outputs::

        mesd_data: (an existing file name)
                MESD data

.. _nipype.interfaces.camino.odf.QBallMX:


.. index:: QBallMX

QBallMX
-------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/camino/odf.py#L39>`__

Wraps command **qballmx**

Generates a reconstruction matrix for Q-Ball. Used in LinRecon with
the same scheme file to reconstruct data.

Example 1
~~~~~~~~~
To create  a linear transform matrix using Spherical Harmonics (sh).

>>> import nipype.interfaces.camino as cam
>>> qballmx = cam.QBallMX()
>>> qballmx.inputs.scheme_file = 'A.scheme'
>>> qballmx.inputs.basistype = 'sh'
>>> qballmx.inputs.order = 6
>>> qballmx.run()            # doctest: +SKIP

Example 2
~~~~~~~~~
To create  a linear transform matrix using Radial Basis Functions
(rbf). This command uses the default setting of rbf sigma = 0.2618
(15 degrees), data smoothing sigma = 0.1309 (7.5 degrees), rbf
pointset 246

>>> import nipype.interfaces.camino as cam
>>> qballmx = cam.QBallMX()
>>> qballmx.inputs.scheme_file = 'A.scheme'
>>> qballmx.run()              # doctest: +SKIP

The linear transform matrix from any of these two examples can then
be run over each voxel using LinRecon

>>> qballcoeffs = cam.LinRecon()
>>> qballcoeffs.inputs.in_file = 'SubjectA.Bfloat'
>>> qballcoeffs.inputs.scheme_file = 'A.scheme'
>>> qballcoeffs.inputs.qball_mat = 'A_qmat.Bdouble'
>>> qballcoeffs.inputs.normalize = True
>>> qballcoeffs.inputs.bgmask = 'brain_mask.nii'
>>> qballcoeffs.run()             # doctest: +SKIP

Inputs::

        [Mandatory]
        scheme_file: (an existing file name)
                Specifies the scheme file for the diffusion MRI data
                flag: -schemefile %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
        basistype: ('rbf' or 'sh', nipype default value: rbf)
                Basis function type. "rbf" to use radial basis functions "sh" to use
                spherical harmonics
                flag: -basistype %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
        order: (an integer)
                Specific to sh. Maximum order of the spherical harmonic series.
                Default is 4.
                flag: -order %d
        out_file: (a file name)
                flag: > %s, position: -1
        rbfpointset: (an integer)
                Specific to rbf. Sets the number of radial basis functions to use.
                The value specified must be present in the Pointsets directory. The
                default value is 246.
                flag: -rbfpointset %d
        rbfsigma: (a float)
                Specific to rbf. Sets the width of the interpolating basis
                functions. The default value is 0.2618 (15 degrees).
                flag: -rbfsigma %f
        smoothingsigma: (a float)
                Specific to rbf. Sets the width of the smoothing basis functions.
                The default value is 0.1309 (7.5 degrees).
                flag: -smoothingsigma %f

Outputs::

        qmat: (an existing file name)
                Q-Ball reconstruction matrix

.. _nipype.interfaces.camino.odf.SFPeaks:


.. index:: SFPeaks

SFPeaks
-------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/camino/odf.py#L336>`__

Wraps command **sfpeaks**

Finds the peaks of spherical functions.

This utility reads coefficients of the spherical functions and
outputs a list of peak directions of the function. It computes the
value of the function at each of a set of sample points. Then it
finds local maxima by finding all points at which the function is
larger than for any other point within a fixed search radius (the
default  is 0.4). The utility then uses Powell's algorithm to
optimize the position of each local maximum. Finally the utility
removes duplicates and tiny peaks with function value smaller than
some threshold, which is the mean of the function plus some number
of standard deviations. By default the program checks for con-
sistency with a second set of starting points, but skips the
optimization step. To speed up execution, you can turn off the con-
sistency check by setting the noconsistencycheck flag to True.

By  default, the utility constructs a set of sample points by
randomly rotating a unit icosahedron repeatedly (the default is 1000
times, which produces a set of 6000 points) and concatenating the
lists of vertices. The 'pointset = <index>' attribute can tell the
utility to use an evenly distributed set of points (index 0 gives
1082 points, 1 gives 1922, 2 gives 4322, 3 gives 8672, 4 gives 15872,
5 gives 32762, 6 gives 72032), which is quicker, because you can get
away with fewer points. We estimate that you can use a factor of 2.5
less evenly distributed points than randomly distributed points and
still expect similar performance levels.

The output for each voxel is:
- exitcode (inherited from the input data).
- ln(A(0))
- number of peaks found.
- flag for consistency with a repeated run (number of directions is
  the same and the directions are the same to within a threshold.)
- mean(f).
- std(f).
- direction 1 (x, y, z, f, H00, H01, H10, H11).
- direction 2 (x, y, z, f, H00, H01, H10, H11).
- direction 3 (x, y, z, f, H00, H01, H10, H11).

H is the Hessian of f at the peak. It is the matrix:
[d^2f/ds^2 d^2f/dsdt]
[d^2f/dtds d^2f/dt^2]
= [H00 H01]
  [H10 H11]
where s and t are orthogonal coordinates local to the peak.

By default the maximum number of peak directions output in each
voxel is three. If less than three directions are found, zeros are
output for later directions. The peaks are ordered by the value of
the function at the peak. If more than the maximum number of
directions are found only the strongest ones are output. The maximum
number can be changed setting the 'numpds' attribute.

The utility can read various kinds of spherical function, but must
be told what kind of function is input using the 'inputmodel'
attribute. The description of the 'inputmodel' attribute lists
additional information required by SFPeaks for each input model.


Example
~~~~~~~~~
First run QBallMX and create a linear transform matrix using
Spherical Harmonics (sh).

>>> import nipype.interfaces.camino as cam
>>> sf_peaks = cam.SFPeaks()
>>> sf_peaks.inputs.in_file = 'A_recon_params.Bdouble'
>>> sf_peaks.inputs.inputmodel = 'sh'
>>> sf_peaks.inputs.order = 4
>>> sf_peaks.inputs.density = 100
>>> sf_peaks.inputs.searchradius = 1.0
>>> sf_peaks.run()          # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                Voxel-order data of spherical functions
                flag: -inputfile %s
        inputmodel: ('sh' or 'maxent' or 'rbf')
                Type of functions input via in_file. Currently supported options
                are: sh - Spherical harmonic series. Specify the maximum order of
                the SH series with the "order" attribute if different from the
                default of 4. maxent - Maximum entropy representations output by
                MESD. The reconstruction directions input to MESD must be specified.
                By default this is the same set of gradient directions (excluding
                zero gradients) in the scheme file, so specify the "schemefile"
                attribute unless the "mepointset" attribute was set in MESD. rbf -
                Sums of radial basis functions. Specify the pointset with the
                attribute "rbfpointset" if different from the default. See QBallMX.
                flag: -inputmodel %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
        density: (an integer)
                The number of randomly rotated icosahedra to use in constructing the
                set of points for random sampling in the peak finding algorithm.
                Default is 1000, which works well for very spiky maxent functions.
                For other types of function, it is reasonable to set the density
                much lower and increase the search radius slightly, which speeds up
                the computation.
                flag: -density %d
        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
        mepointset: (an integer)
                Use a set of directions other than those in the scheme file for the
                deconvolution kernel. The number refers to the number of directions
                on the unit sphere. For example, "mepointset = 54" uses the
                directions in "camino/PointSets/Elec054.txt" Use this option only if
                you told MESD to use a custom set of directions with the same
                option. Otherwise, specify the scheme file with the "schemefile"
                attribute.
                flag: -mepointset %d
        noconsistencycheck: (a boolean)
                Turns off the consistency check. The output shows all consistencies
                as true.
                flag: -noconsistencycheck
        numpds: (an integer)
                The largest number of peak directions to output in each voxel.
                flag: -numpds %d
        order: (an integer)
                Specific to sh. Maximum order of the spherical harmonic series.
                flag: -order %d
        out_file: (a file name)
                flag: > %s, position: -1
        pdthresh: (a float)
                Base threshold on the actual peak direction strength divided by the
                mean of the function. The default is 1.0 (the peak must be equal or
                greater than the mean).
                flag: -pdthresh %f
        pointset: (an integer)
                To sample using an evenly distributed set of points instead. The
                integer can be 0, 1, ..., 7. Index 0 gives 1082 points, 1 gives
                1922, 2 gives 3002, 3 gives 4322, 4 gives 5882, 5 gives 8672, 6
                gives 12002, 7 gives 15872.
                flag: -pointset %d
        rbfpointset: (an integer)
                Specific to rbf. Sets the number of radial basis functions to use.
                The value specified must be present in the Pointsets directory. The
                default value is 246.
                flag: -rbfpointset %d
        scheme_file: (an existing file name)
                Specific to maxent. Specifies the scheme file.
                flag: %s
        searchradius: (a float)
                The search radius in the peak finding algorithm. The default is 0.4
                (cf. "density")
                flag: -searchradius %f
        stdsfrommean: (a float)
                This is the number of standard deviations of the function to be
                added to the "pdthresh" attribute in the peak directions pruning.
                flag: -stdsfrommean %f

Outputs::

        peaks: (an existing file name)
                Peaks of the spherical functions.
