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# nipype.interfaces.camino.dti module

## ComputeEigensystem

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `dteig`.

> Computes the eigensystem from tensor fitted data.

> Reads diffusion tensor (single, two-tensor, three-tensor or multitensor) data from the standard input, computes the eigenvalues and eigenvectors of each tensor and outputs the results to the standard output. For multiple-tensor data the program outputs the eigensystem of each tensor. For each tensor the program outputs: {l_1, e_11, e_12, e_13, l_2, e_21, e_22, e_33, l_3, e_31, e_32, e_33}, where l_1 >= l_2 >= l_3 and e_i = (e_i1, e_i2, e_i3) is the eigenvector with eigenvalue l_i. For three-tensor data, for example, the output contains thirty-six values per voxel.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> dteig = cmon.ComputeEigensystem()
>     >>> dteig.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> dteig.run()

> in_filea pathlike object or string representing an existing file

>   Tensor-fitted data filename. Maps to a command-line argument: `< %s` (position: 1).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputdatatype‘double’ or ‘float’ or ‘long’ or ‘int’ or ‘short’ or ‘char’

>   Specifies the data type of the input data. The data type can be any of the following strings: “char”, “short”, “int”, “long”, “float” or “double”.Default is double data type. Maps to a command-line argument: `-inputdatatype %s`. (Nipype default value: `double`)

> inputmodel‘dt’ or ‘multitensor’

>   Specifies the model that the input data contains parameters for. Maps to a command-line argument: `-inputmodel %s`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel of the input data. Maps to a command-line argument: `-maxcomponents %d`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> outputdatatype‘double’ or ‘float’ or ‘long’ or ‘int’ or ‘short’ or ‘char’

>   Specifies the data type of the output data. Maps to a command-line argument: `-outputdatatype %s`. (Nipype default value: `double`)

> eigena pathlike object or string representing an existing file

>   Trace of the diffusion tensor.

## ComputeFractionalAnisotropy

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `fa`.

> Computes the fractional anisotropy of tensors.

> Reads diffusion tensor (single, two-tensor or three-tensor) data from the standard input, computes the fractional anisotropy (FA) of each tensor and outputs the results to the standard output. For multiple-tensor data the program outputs the FA of each tensor, so for three-tensor data, for example, the output contains three fractional anisotropy values per voxel.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> fa = cmon.ComputeFractionalAnisotropy()
>     >>> fa.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> fa.inputs.scheme_file = 'A.scheme'
>     >>> fa.run()

> in_filea pathlike object or string representing an existing file

>   Tensor-fitted data filename. Maps to a command-line argument: `< %s` (position: 1).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the input file. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘twotensor’ or ‘threetensor’ or ‘multitensor’

>   Specifies the model that the input tensor data contains parameters for. By default, the program assumes that the input data contains a single diffusion tensor in each voxel. Maps to a command-line argument: `-inputmodel %s`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> outputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the output data. Maps to a command-line argument: `-outputdatatype %s`.

> scheme_filea pathlike object or string representing an existing file

>   Camino scheme file (b values / vectors, see camino.fsl2scheme). Maps to a command-line argument: `%s` (position: 2).

> faa pathlike object or string representing an existing file

>   Fractional Anisotropy Map.

## ComputeMeanDiffusivity

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `md`.

> Computes the mean diffusivity (trace/3) from diffusion tensors.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> md = cmon.ComputeMeanDiffusivity()
>     >>> md.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> md.inputs.scheme_file = 'A.scheme'
>     >>> md.run()

> in_filea pathlike object or string representing an existing file

>   Tensor-fitted data filename. Maps to a command-line argument: `< %s` (position: 1).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the input file. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘twotensor’ or ‘threetensor’

>   Specifies the model that the input tensor data contains parameters for. By default, the program assumes that the input data contains a single diffusion tensor in each voxel. Maps to a command-line argument: `-inputmodel %s`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> outputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the output data. Maps to a command-line argument: `-outputdatatype %s`.

> scheme_filea pathlike object or string representing an existing file

>   Camino scheme file (b values / vectors, see camino.fsl2scheme). Maps to a command-line argument: `%s` (position: 2).

> mda pathlike object or string representing an existing file

>   Mean Diffusivity Map.

## ComputeTensorTrace

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `trd`.

> Computes the trace of tensors.

> Reads diffusion tensor (single, two-tensor or three-tensor) data from the standard input, computes the trace of each tensor, i.e., three times the mean diffusivity, and outputs the results to the standard output. For multiple-tensor data the program outputs the trace of each tensor, so for three-tensor data, for example, the output contains three values per voxel.

> Divide the output by three to get the mean diffusivity.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> trace = cmon.ComputeTensorTrace()
>     >>> trace.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> trace.inputs.scheme_file = 'A.scheme'
>     >>> trace.run()

> in_filea pathlike object or string representing an existing file

>   Tensor-fitted data filename. Maps to a command-line argument: `< %s` (position: 1).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the input file. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘twotensor’ or ‘threetensor’ or ‘multitensor’

>   Specifies the model that the input tensor data contains parameters for. By default, the program assumes that the input data contains a single diffusion tensor in each voxel. Maps to a command-line argument: `-inputmodel %s`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> outputdatatype‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘float’ or ‘double’

>   Specifies the data type of the output data. Maps to a command-line argument: `-outputdatatype %s`.

> scheme_filea pathlike object or string representing an existing file

>   Camino scheme file (b values / vectors, see camino.fsl2scheme). Maps to a command-line argument: `%s` (position: 2).

> tracea pathlike object or string representing an existing file

>   Trace of the diffusion tensor.

## DTIFit

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `dtfit`.

> Reads diffusion MRI data, acquired using the acquisition scheme detailed in the scheme file, from the data file.

> Use non-linear fitting instead of the default linear regression to the log measurements. The data file stores the diffusion MRI data in voxel order with the measurements stored in big-endian format and ordered as in the scheme file. The default input data type is four-byte float. The default output data type is eight-byte double. See modelfit and camino for the format of the data file and scheme file. The program fits the diffusion tensor to each voxel and outputs the results, in voxel order and as big-endian eight-byte doubles, to the standard output. The program outputs eight values in each voxel: [exit code, ln(S(0)), D_xx, D_xy, D_xz, D_yy, D_yz, D_zz]. An exit code of zero indicates no problems. For a list of other exit codes, see modelfit(1). The entry S(0) is an estimate of the signal at q=0.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> fit = cmon.DTIFit()
>     >>> fit.inputs.scheme_file = 'A.scheme'
>     >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> fit.run()

> in_filea pathlike object or string representing an existing file

>   Voxel-order data filename. Maps to a command-line argument: `%s` (position: 1).

> scheme_filea pathlike object or string representing an existing file

>   Camino scheme file (b values / vectors, see camino.fsl2scheme). Maps to a command-line argument: `%s` (position: 2).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bgmaska pathlike object or string representing an existing file

>   Provides the name of a file containing a background mask computed using, for example, FSL bet2 program. The mask file contains zero in background voxels and non-zero in foreground. Maps to a command-line argument: `-bgmask %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> non_lineara boolean

>   Use non-linear fitting instead of the default linear regression to the log measurements. . Maps to a command-line argument: `-nonlinear` (position: 3).

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> tensor_fitteda pathlike object or string representing an existing file

>   Path/name of 4D volume in voxel order.

## DTLUTGen

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `dtlutgen`.

> Calibrates the PDFs for PICo probabilistic tractography.

> This program needs to be run once for every acquisition scheme. It outputs a lookup table that is used by the dtpicoparams program to find PICo PDF parameters for an image. The default single tensor LUT contains parameters of the Bingham distribution and is generated by supplying a scheme file and an estimated signal to noise in white matter regions of the (q=0) image. The default inversion is linear (inversion index 1).

> Advanced users can control several options, including the extent and resolution of the LUT, the inversion index, and the type of PDF. See dtlutgen(1) for details.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> dtl = cmon.DTLUTGen()
>     >>> dtl.inputs.snr = 16
>     >>> dtl.inputs.scheme_file = 'A.scheme'
>     >>> dtl.run()

> scheme_filea pathlike object or string representing a file

>   The scheme file of the images to be processed using this LUT. Maps to a command-line argument: `-schemefile %s` (position: 2).

> acga boolean

>   Compute a LUT for the ACG PDF. Maps to a command-line argument: `-acg`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> binghama boolean

>   Compute a LUT for the Bingham PDF. This is the default. Maps to a command-line argument: `-bingham`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> frangea list of from 2 to 2 items which are a float

>   Index to two-tensor LUTs. This is the fractional anisotropy of the two tensors. The default is 0.3 to 0.94. Maps to a command-line argument: `-frange %s` (position: 1).

> inversionan integer

>   Index of the inversion to use. The default is 1 (linear single tensor inversion). Maps to a command-line argument: `-inversion %d`.

> lrangea list of from 2 to 2 items which are a float

>   Index to one-tensor LUTs. This is the ratio L1/L3 and L2 / L3.The LUT is square, with half the values calculated (because L2 / L3 cannot be less than L1 / L3 by definition).The minimum must be >= 1. For comparison, a ratio L1 / L3 = 10 with L2 / L3 = 1 corresponds to an FA of 0.891, and L1 / L3 = 15 with L2 / L3 = 1 corresponds to an FA of 0.929. The default range is 1 to 10. Maps to a command-line argument: `-lrange %s` (position: 1).

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> samplesan integer

>   The number of synthetic measurements to generate at each point in the LUT. The default is 2000. Maps to a command-line argument: `-samples %d`.

> snra float

>   The signal to noise ratio of the unweighted (q = 0) measurements.This should match the SNR (in white matter) of the images that the LUTs are used with. Maps to a command-line argument: `-snr %f`.

> stepa float

>   Distance between points in the LUT.For example, if lrange is 1 to 10 and the step is 0.1, LUT entries will be computed at L1 / L3 = 1, 1.1, 1.2 … 10.0 and at L2 / L3 = 1.0, 1.1 … L1 / L3.For single tensor LUTs, the default step is 0.2, for two-tensor LUTs it is 0.02. Maps to a command-line argument: `-step %f`.

> tracea float

>   Trace of the diffusion tensor(s) used in the test function in the LUT generation. The default is 2100E-12 m^2 s^-1. Maps to a command-line argument: `-trace %G`.

> watsona boolean

>   Compute a LUT for the Watson PDF. Maps to a command-line argument: `-watson`.

> dtLUTa pathlike object or string representing an existing file

>   Lookup Table.

## DTMetric

Link to code

> Bases: CommandLine

> Wrapped executable: `dtshape`.

> Computes tensor metric statistics based on the eigenvalues l1 >= l2 >= l3 typically obtained from ComputeEigensystem.

> The full list of statistics is:

> > - <cl> = (l1 - l2) / l1 , a measure of linearity

> > - <cp> = (l2 - l3) / l1 , a measure of planarity

> > - <cs> = l3 / l1 , a measure of isotropy with: cl + cp + cs = 1

> > - <l1> = first eigenvalue

> > - <l2> = second eigenvalue

> > - <l3> = third eigenvalue

> > - <tr> = l1 + l2 + l3

> > - <md> = tr / 3

> > - <rd> = (l2 + l3) / 2

> > - <fa> = fractional anisotropy. (Basser et al, J Magn Reson B 1996)

> > - <ra> = relative anisotropy (Basser et al, J Magn Reson B 1996)

> > - <2dfa> = 2D FA of the two minor eigenvalues l2 and l3 i.e. sqrt( 2 * [(l2 - <l>)^2 + (l3 - <l>)^2] / (l2^2 + l3^2) ) with: <l> = (l2 + l3) / 2

> Example

> Compute the CP planar metric as float data type.

>     >>> import nipype.interfaces.camino as cam
>     >>> dtmetric = cam.DTMetric()
>     >>> dtmetric.inputs.eigen_data = 'dteig.Bdouble'
>     >>> dtmetric.inputs.metric = 'cp'
>     >>> dtmetric.inputs.outputdatatype = 'float'
>     >>> dtmetric.run()

> eigen_dataa pathlike object or string representing an existing file

>   Voxel-order data filename. Maps to a command-line argument: `-inputfile %s`.

> metric‘fa’ or ‘md’ or ‘rd’ or ‘l1’ or ‘l2’ or ‘l3’ or ‘tr’ or ‘ra’ or ‘2dfa’ or ‘cl’ or ‘cp’ or ‘cs’

>   Specifies the metric to compute. Maps to a command-line argument: `-stat %s`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> data_headera pathlike object or string representing an existing file

>   A Nifti .nii or .nii.gz file containing the header information. Usually this will be the header of the raw data file from which the diffusion tensors were reconstructed. Maps to a command-line argument: `-header %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputdatatype‘double’ or ‘float’ or ‘long’ or ‘int’ or ‘short’ or ‘char’

>   Specifies the data type of the input data. Maps to a command-line argument: `-inputdatatype %s`. (Nipype default value: `double`)

> outputdatatype‘double’ or ‘float’ or ‘long’ or ‘int’ or ‘short’ or ‘char’

>   Specifies the data type of the output data. Maps to a command-line argument: `-outputdatatype %s`. (Nipype default value: `double`)

> outputfilea pathlike object or string representing a file

>   Output name. Output will be a .nii.gz file if data_header is provided andin voxel order with outputdatatype datatype (default: double) otherwise. Maps to a command-line argument: `-outputfile %s`.

> metric_statsa pathlike object or string representing an existing file

>   Diffusion Tensor statistics of the chosen metric.

## ModelFit

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `modelfit`.

> Fits models of the spin-displacement density to diffusion MRI measurements.

> This is an interface to various model fitting routines for diffusion MRI data that fit models of the spin-displacement density function. In particular, it will fit the diffusion tensor to a set of measurements as well as various other models including two or three-tensor models. The program can read input data from a file or can generate synthetic data using various test functions for testing and simulations.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> fit = cmon.ModelFit()
>     >>> fit.model = 'dt'
>     >>> fit.inputs.scheme_file = 'A.scheme'
>     >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> fit.run()

> in_filea pathlike object or string representing an existing file

>   Voxel-order data filename. Maps to a command-line argument: `-inputfile %s`.

> model‘dt’ or ‘restore’ or ‘algdt’ or ‘nldt_pos’ or ‘nldt’ or ‘ldt_wtd’ or ‘adc’ or ‘ball_stick’ or ‘cylcyl dt’ or ‘cylcyl restore’ or ‘cylcyl algdt’ or ‘cylcyl nldt_pos’ or ‘cylcyl nldt’ or ‘cylcyl ldt_wtd’ or ‘cylcyl adc’ or ‘cylcyl ball_stick’ or ‘cylcyl_eq dt’ or ‘cylcyl_eq restore’ or ‘cylcyl_eq algdt’ or ‘cylcyl_eq nldt_pos’ or ‘cylcyl_eq nldt’ or ‘cylcyl_eq ldt_wtd’ or ‘cylcyl_eq adc’ or ‘cylcyl_eq ball_stick’ or ‘pospos dt’ or ‘pospos restore’ or ‘pospos algdt’ or ‘pospos nldt_pos’ or ‘pospos nldt’ or ‘pospos ldt_wtd’ or ‘pospos adc’ or ‘pospos ball_stick’ or ‘pospos_eq dt’ or ‘pospos_eq restore’ or ‘pospos_eq algdt’ or ‘pospos_eq nldt_pos’ or ‘pospos_eq nldt’ or ‘pospos_eq ldt_wtd’ or ‘pospos_eq adc’ or ‘pospos_eq ball_stick’ or ‘poscyl dt’ or ‘poscyl restore’ or ‘poscyl algdt’ or ‘poscyl nldt_pos’ or ‘poscyl nldt’ or ‘poscyl ldt_wtd’ or ‘poscyl adc’ or ‘poscyl ball_stick’ or ‘poscyl_eq dt’ or ‘poscyl_eq restore’ or ‘poscyl_eq algdt’ or ‘poscyl_eq nldt_pos’ or ‘poscyl_eq nldt’ or ‘poscyl_eq ldt_wtd’ or ‘poscyl_eq adc’ or ‘poscyl_eq ball_stick’ or ‘cylcylcyl dt’ or ‘cylcylcyl restore’ or ‘cylcylcyl algdt’ or ‘cylcylcyl nldt_pos’ or ‘cylcylcyl nldt’ or ‘cylcylcyl ldt_wtd’ or ‘cylcylcyl adc’ or ‘cylcylcyl ball_stick’ or ‘cylcylcyl_eq dt’ or ‘cylcylcyl_eq restore’ or ‘cylcylcyl_eq algdt’ or ‘cylcylcyl_eq nldt_pos’ or ‘cylcylcyl_eq nldt’ or ‘cylcylcyl_eq ldt_wtd’ or ‘cylcylcyl_eq adc’ or ‘cylcylcyl_eq ball_stick’ or ‘pospospos dt’ or ‘pospospos restore’ or ‘pospospos algdt’ or ‘pospospos nldt_pos’ or ‘pospospos nldt’ or ‘pospospos ldt_wtd’ or ‘pospospos adc’ or ‘pospospos ball_stick’ or ‘pospospos_eq dt’ or ‘pospospos_eq restore’ or ‘pospospos_eq algdt’ or ‘pospospos_eq nldt_pos’ or ‘pospospos_eq nldt’ or ‘pospospos_eq ldt_wtd’ or ‘pospospos_eq adc’ or ‘pospospos_eq ball_stick’ or ‘posposcyl dt’ or ‘posposcyl restore’ or ‘posposcyl algdt’ or ‘posposcyl nldt_pos’ or ‘posposcyl nldt’ or ‘posposcyl ldt_wtd’ or ‘posposcyl adc’ or ‘posposcyl ball_stick’ or ‘posposcyl_eq dt’ or ‘posposcyl_eq restore’ or ‘posposcyl_eq algdt’ or ‘posposcyl_eq nldt_pos’ or ‘posposcyl_eq nldt’ or ‘posposcyl_eq ldt_wtd’ or ‘posposcyl_eq adc’ or ‘posposcyl_eq ball_stick’ or ‘poscylcyl dt’ or ‘poscylcyl restore’ or ‘poscylcyl algdt’ or ‘poscylcyl nldt_pos’ or ‘poscylcyl nldt’ or ‘poscylcyl ldt_wtd’ or ‘poscylcyl adc’ or ‘poscylcyl ball_stick’ or ‘poscylcyl_eq dt’ or ‘poscylcyl_eq restore’ or ‘poscylcyl_eq algdt’ or ‘poscylcyl_eq nldt_pos’ or ‘poscylcyl_eq nldt’ or ‘poscylcyl_eq ldt_wtd’ or ‘poscylcyl_eq adc’ or ‘poscylcyl_eq ball_stick’

>   Specifies the model to be fit to the data. Maps to a command-line argument: `-model %s`.

> scheme_filea pathlike object or string representing an existing file

>   Camino scheme file (b values / vectors, see camino.fsl2scheme). Maps to a command-line argument: `-schemefile %s`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bgmaska pathlike object or string representing an existing file

>   Provides the name of a file containing a background mask computed using, for example, FSL’s bet2 program. The mask file contains zero in background voxels and non-zero in foreground. Maps to a command-line argument: `-bgmask %s`.

> bgthresha float

>   Sets a threshold on the average q=0 measurement to separate foreground and background. The program does not process background voxels, but outputs the same number of values in background voxels and foreground voxels. Each value is zero in background voxels apart from the exit code which is -1. Maps to a command-line argument: `-bgthresh %G`.

> cfthresha float

>   Sets a threshold on the average q=0 measurement to determine which voxels are CSF. This program does not treat CSF voxels any different to other voxels. Maps to a command-line argument: `-csfthresh %G`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> fixedbvaluea list of from 3 to 3 items which are a float

>   As above, but specifies <M> <N> <b>. The resulting scheme is the same whether you specify b directly or indirectly using -fixedmodq. Maps to a command-line argument: `-fixedbvalue %s`.

> fixedmodqa list of from 4 to 4 items which are a float

>   Specifies <M> <N> <Q> <tau> a spherical acquisition scheme with M measurements with q=0 and N measurements with \(|q|=Q\) and diffusion time tau. The N measurements with \(|q|=Q\) have unique directions. The program reads in the directions from the files in directory PointSets. Maps to a command-line argument: `-fixedmod %s`.

> inputdatatype‘float’ or ‘char’ or ‘short’ or ‘int’ or ‘long’ or ‘double’

>   Specifies the data type of the input file. The input file must have BIG-ENDIAN ordering. By default, the input type is `float`. Maps to a command-line argument: `-inputdatatype %s`.

> noisemapa pathlike object or string representing an existing file

>   Specifies the name of the file to contain the estimated noise variance on the diffusion-weighted signal, generated by a weighted tensor fit. The data type of this file is big-endian double. Maps to a command-line argument: `-noisemap %s`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> outliera pathlike object or string representing an existing file

>   Specifies the name of the file to contain the outlier map generated by the RESTORE algorithm. Maps to a command-line argument: `-outliermap %s`.

> outputfilea pathlike object or string representing a file

>   Filename of the output file. Maps to a command-line argument: `-outputfile %s`.

> residualmapa pathlike object or string representing an existing file

>   Specifies the name of the file to contain the weighted residual errors after computing a weighted linear tensor fit. One value is produced per measurement, in voxel order. The data type of this file is big-endian double. Images of the residuals for each measurement can be extracted with shredder. Maps to a command-line argument: `-residualmap %s`.

> sigmaa float

>   Specifies the standard deviation of the noise in the data. Required by the RESTORE algorithm. Maps to a command-line argument: `-sigma %G`.

> taua float

>   Sets the diffusion time separately. This overrides the diffusion time specified in a scheme file or by a scheme index for both the acquisition scheme and in the data synthesis. Maps to a command-line argument: `-tau %G`.

> fitted_dataa pathlike object or string representing an existing file

>   Output file of 4D volume in voxel order.

## PicoPDFs

Link to code

> Bases: StdOutCommandLine

> Wrapped executable: `picopdfs`.

> Constructs a spherical PDF in each voxel for probabilistic tractography.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> pdf = cmon.PicoPDFs()
>     >>> pdf.inputs.inputmodel = 'dt'
>     >>> pdf.inputs.luts = ['lut_file']
>     >>> pdf.inputs.in_file = 'voxel-order_data.Bfloat'
>     >>> pdf.run()

> in_filea pathlike object or string representing an existing file

>   Voxel-order data filename. Maps to a command-line argument: `< %s` (position: 1).

> lutsa list of items which are a pathlike object or string representing an existing file

>   Files containing the lookup tables.For tensor data, one lut must be specified for each type of inversion used in the image (one-tensor, two-tensor, three-tensor).For pds, the number of LUTs must match -numpds (it is acceptable to use the same LUT several times - see example, above).These LUTs may be generated with dtlutgen. Maps to a command-line argument: `-luts %s`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> directmapa boolean

>   Only applicable when using pds as the inputmodel. Use direct mapping between the eigenvalues and the distribution parameters instead of the log of the eigenvalues. Maps to a command-line argument: `-directmap`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> inputmodel‘dt’ or ‘multitensor’ or ‘pds’

>   Input model type. Maps to a command-line argument: `-inputmodel %s` (position: 2). (Nipype default value: `dt`)

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel (default 2) for multitensor data.Currently, only the default is supported, but future releases may allow the input of three-tensor data using this option. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel (default 3) for PD data.This option determines the size of the input and output voxels.This means that the data file may be large enough to accommodate three or more PDs,but does not mean that any of the voxels are classified as containing three or more PDs. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Maps to a command-line argument: `> %s` (position: -1).

> pdf‘bingham’ or ‘watson’ or ‘acg’

>   Specifies the PDF to use. There are three choices:

>   > - watson - The Watson distribution. This distribution is rotationally symmetric.

>   > - bingham - The Bingham distributionn, which allows elliptical probability density contours.

>   > - acg - The Angular Central Gaussian distribution, which also allows elliptical probability density contours.

>   Maps to a command-line argument: `-pdf %s` (position: 4). (Nipype default value: `bingham`)

> pdfsa pathlike object or string representing an existing file

>   Path/name of 4D volume in voxel order.

## Track

Link to code

> Bases: CommandLine

> Wrapped executable: `track`.

> Performs tractography using one of the following models: dt’, ‘multitensor’, ‘pds’, ‘pico’, ‘bootstrap’, ‘ballstick’, ‘bayesdirac’

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.Track()
>     >>> track.inputs.inputmodel = 'dt'
>     >>> track.inputs.in_file = 'data.Bfloat'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackBallStick

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Performs streamline tractography using ball-stick fitted data

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.TrackBallStick()
>     >>> track.inputs.in_file = 'ballstickfit_data.Bfloat'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackBayesDirac

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Perform streamline tractography using a Bayesian tracking with Dirac priors.

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.TrackBayesDirac()
>     >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.inputs.scheme_file = 'bvecs.scheme'
>     >>> track.run()

> scheme_filea pathlike object or string representing an existing file

>   The scheme file corresponding to the data being processed. Maps to a command-line argument: `-schemefile %s`.

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvepriorga float

>   Concentration parameter for the prior distribution on fibre orientations given the fibre orientation at the previous step. Larger values of g make curvature less likely. Maps to a command-line argument: `-curvepriorg %G`.

> curvepriorka float

>   Concentration parameter for the prior distribution on fibre orientations given the fibre orientation at the previous step. Larger values of k make curvature less likely. Maps to a command-line argument: `-curvepriork %G`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> datamodel‘cylsymmdt’ or ‘ballstick’

>   Model of the data for Bayesian tracking. The default model is “cylsymmdt”, a diffusion tensor with cylindrical symmetry about e_1, ie L1 >= L_2 = L_3. The other model is “ballstick”, the partial volume model (see ballstickfit). Maps to a command-line argument: `-datamodel %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> extpriordatatype‘float’ or ‘double’

>   Datatype of the prior image. The default is “double”. Maps to a command-line argument: `-extpriordatatype %s`.

> extpriorfilea pathlike object or string representing an existing file

>   Path to a PICo image produced by picopdfs. The PDF in each voxel is used as a prior for the fibre orientation in Bayesian tracking. The prior image must be in the same space as the diffusion data. Maps to a command-line argument: `-extpriorfile %s`.

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> iterationsan integer

>   Number of streamlines to generate at each seed point. The default is 5000. Maps to a command-line argument: `-iterations %d`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> pdf‘bingham’ or ‘watson’ or ‘acg’

>   Specifies the model for PICo priors (not the curvature priors). The default is ‘bingham’. Maps to a command-line argument: `-pdf %s`.

> pointsetan integer

>   Index to the point set to use for Bayesian likelihood calculation. The index specifies a set of evenly distributed points on the unit sphere, where each point x defines two possible step directions (x or -x) for the streamline path. A larger number indexes a larger point set, which gives higher angular resolution at the expense of computation time. The default is index 1, which gives 1922 points, index 0 gives 1082 points, index 2 gives 3002 points. Maps to a command-line argument: `-pointset %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackBedpostxDeter

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Data from FSL’s bedpostx can be imported into Camino for deterministic tracking. (Use TrackBedpostxProba for bedpostx probabilistic tractography.)

> The tracking is based on the vector images dyads1.nii.gz, … , dyadsN.nii.gz, where there are a maximum of N compartments (corresponding to each fiber population) in each voxel.

> It also uses the N images mean_f1samples.nii.gz, …, mean_fNsamples.nii.gz, normalized such that the sum of all compartments is 1. Compartments where the mean_f is less than a threshold are discarded and not used for tracking. The default value is 0.01. This can be changed with the min_vol_frac option.

> Example

>     >>> import nipype.interfaces.camino as cam
>     >>> track = cam.TrackBedpostxDeter()
>     >>> track.inputs.bedpostxdir = 'bedpostxout'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> bedpostxdira pathlike object or string representing an existing directory

>   Directory containing bedpostx output. Maps to a command-line argument: `-bedpostxdir %s`.

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> min_vol_fraca float

>   Zeros out compartments in bedpostx data with a mean volume fraction f of less than min_vol_frac. The default is 0.01. Maps to a command-line argument: `-bedpostxminf %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackBedpostxProba

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Data from FSL’s bedpostx can be imported into Camino for probabilistic tracking. (Use TrackBedpostxDeter for bedpostx deterministic tractography.)

> The tracking uses the files merged_th1samples.nii.gz, merged_ph1samples.nii.gz, … , merged_thNsamples.nii.gz, merged_phNsamples.nii.gz where there are a maximum of N compartments (corresponding to each fiber population) in each voxel. These images contain M samples of theta and phi, the polar coordinates describing the “stick” for each compartment. At each iteration, a random number X between 1 and M is drawn and the Xth samples of theta and phi become the principal directions in the voxel.

> It also uses the N images mean_f1samples.nii.gz, …, mean_fNsamples.nii.gz, normalized such that the sum of all compartments is 1. Compartments where the mean_f is less than a threshold are discarded and not used for tracking. The default value is 0.01. This can be changed with the min_vol_frac option.

> Example

>     >>> import nipype.interfaces.camino as cam
>     >>> track = cam.TrackBedpostxProba()
>     >>> track.inputs.bedpostxdir = 'bedpostxout'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.inputs.iterations = 100
>     >>> track.run()

> bedpostxdira pathlike object or string representing an existing directory

>   Directory containing bedpostx output. Maps to a command-line argument: `-bedpostxdir %s`.

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> iterationsan integer

>   Number of streamlines to generate at each seed point. The default is 1. Maps to a command-line argument: `-iterations %d`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> min_vol_fraca float

>   Zeros out compartments in bedpostx data with a mean volume fraction f of less than min_vol_frac. The default is 0.01. Maps to a command-line argument: `-bedpostxminf %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackBootstrap

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Performs bootstrap streamline tractography using multiple scans of the same subject

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.TrackBootstrap()
>     >>> track.inputs.inputmodel='repbs_dt'
>     >>> track.inputs.scheme_file = 'bvecs.scheme'
>     >>> track.inputs.bsdatafiles = ['fitted_data1.Bfloat', 'fitted_data2.Bfloat']
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> bsdatafilesa list of items which are a pathlike object or string representing an existing file

>   Specifies files containing raw data for repetition bootstrapping. Use -inputfile for wild bootstrap data. Maps to a command-line argument: `-bsdatafile %s`.

> scheme_filea pathlike object or string representing an existing file

>   The scheme file corresponding to the data being processed. Maps to a command-line argument: `-schemefile %s`.

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> bgmaska pathlike object or string representing an existing file

>   Provides the name of a file containing a background mask computed using, for example, FSL’s bet2 program. The mask file contains zero in background voxels and non-zero in foreground. Maps to a command-line argument: `-bgmask %s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> inversionan integer

>   Tensor reconstruction algorithm for repetition bootstrapping. Default is 1 (linear reconstruction, single tensor). Maps to a command-line argument: `-inversion %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> iterationsan integer

>   Number of streamlines to generate at each seed point. Maps to a command-line argument: `-iterations %d`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackDT

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Performs streamline tractography using tensor data

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.TrackDT()
>     >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.

## TrackPICo

Link to code

> Bases: Track

> Wrapped executable: `track`.

> Performs streamline tractography using Probabilistic Index of Connectivity (PICo).

> Example

>     >>> import nipype.interfaces.camino as cmon
>     >>> track = cmon.TrackPICo()
>     >>> track.inputs.in_file = 'pdfs.Bfloat'
>     >>> track.inputs.seed_file = 'seed_mask.nii'
>     >>> track.run()

> anisfilea pathlike object or string representing an existing file

>   File containing the anisotropy map. This is required to apply an anisotropy threshold with non tensor data. If the map issupplied it is always used, even in tensor data. Maps to a command-line argument: `-anisfile %s`.

> anisthresha float

>   Terminate fibres that enter a voxel with lower anisotropy than the threshold. Maps to a command-line argument: `-anisthresh %f`.

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> curveintervala float

>   Interval over which the curvature threshold should be evaluated, in mm. The default is 5mm. When using the default curvature threshold of 90 degrees, this means that streamlines will terminate if they curve by more than 90 degrees over a path length of 5mm. Maps to a command-line argument: `-curveinterval %f`. Requires inputs: `curvethresh`.

> curvethresha float

>   Curvature threshold for tracking, expressed as the maximum angle (in degrees) between between two streamline orientations calculated over the length of a voxel. If the angle is greater than this, then the streamline terminates. Maps to a command-line argument: `-curvethresh %f`.

> data_dimsa list of from 3 to 3 items which are an integer

>   Data dimensions in voxels. Maps to a command-line argument: `-datadims %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> gzipa boolean

>   Save the output image in gzip format. Maps to a command-line argument: `-gzip`.

> in_filea pathlike object or string representing an existing file

>   Input data file. Maps to a command-line argument: `-inputfile %s` (position: 1).

> inputdatatype‘float’ or ‘double’

>   Input file type. Maps to a command-line argument: `-inputdatatype %s`.

> inputmodel‘dt’ or ‘multitensor’ or ‘sfpeak’ or ‘pico’ or ‘repbs_dt’ or ‘repbs_multitensor’ or ‘ballstick’ or ‘wildbs_dt’ or ‘bayesdirac’ or ‘bayesdirac_dt’ or ‘bedpostx_dyad’ or ‘bedpostx’

>   Input model type. Maps to a command-line argument: `-inputmodel %s`. (Nipype default value: `dt`)

> interpolator‘nn’ or ‘prob_nn’ or ‘linear’

>   The interpolation algorithm determines how the fiber orientation(s) are defined at a given continuous point within the input image. Interpolators are only used when the tracking algorithm is not FACT. The choices are: - NN: Nearest-neighbour interpolation, just uses the local voxel data directly.- PROB_NN: Probabilistic nearest-neighbor interpolation, similar to the method pro- posed by Behrens et al [Magnetic Resonance in Medicine, 50:1077-1088, 2003]. The data is not interpolated, but at each point we randomly choose one of the 8 voxels sur- rounding a point. The probability of choosing a particular voxel is based on how close the point is to the centre of that voxel.- LINEAR: Linear interpolation of the vector field containing the principal directions at each point. Maps to a command-line argument: `-interpolator %s`.

> ipthresha float

>   Curvature threshold for tracking, expressed as the minimum dot product between two streamline orientations calculated over the length of a voxel. If the dot product between the previous and current directions is less than this threshold, then the streamline terminates. The default setting will terminate fibres that curve by more than 80 degrees. Set this to -1.0 to disable curvature checking completely. Maps to a command-line argument: `-ipthresh %f`.

> iterationsan integer

>   Number of streamlines to generate at each seed point. The default is 5000. Maps to a command-line argument: `-iterations %d`.

> maxcomponentsan integer

>   The maximum number of tensor components in a voxel. This determines the size of the input file and does not say anything about the voxel classification. The default is 2 if the input model is multitensor and 1 if the input model is dt. Maps to a command-line argument: `-maxcomponents %d`.

> numpdsan integer

>   The maximum number of PDs in a voxel for input models sfpeak and pico. The default is 3 for input model sfpeak and 1 for input model pico. This option determines the size of the voxels in the input file and does not affect tracking. For tensor data, use the -maxcomponents option. Maps to a command-line argument: `-numpds %d`.

> out_filea pathlike object or string representing a file

>   Output data file. Maps to a command-line argument: `-outputfile %s` (position: -1).

> output_roota pathlike object or string representing a file

>   Root directory for output. Maps to a command-line argument: `-outputroot %s` (position: -1).

> outputtracts‘float’ or ‘double’ or ‘oogl’

>   Output tract file type. Maps to a command-line argument: `-outputtracts %s`.

> pdf‘bingham’ or ‘watson’ or ‘acg’

>   Specifies the model for PICo parameters. The default is “bingham. Maps to a command-line argument: `-pdf %s`.

> seed_filea pathlike object or string representing an existing file

>   Seed file. Maps to a command-line argument: `-seedfile %s` (position: 2).

> stepsizea float

>   Step size for EULER and RK4 tracking. The default is 1mm. Maps to a command-line argument: `-stepsize %f`. Requires inputs: `tracker`.

> tracker‘fact’ or ‘euler’ or ‘rk4’

>   The tracking algorithm controls streamlines are generated from the data. The choices are: - FACT, which follows the local fibre orientation in each voxel. No interpolation is used.- EULER, which uses a fixed step size along the local fibre orientation. With nearest-neighbour interpolation, this method may be very similar to FACT, except that the step size is fixed, whereas FACT steps extend to the boundary of the next voxel (distance variable depending on the entry and exit points to the voxel).- RK4: Fourth-order Runge-Kutta method. The step size is fixed, however the eventual direction of the step is determined by taking and averaging a series of partial steps. Maps to a command-line argument: `-tracker %s`. (Nipype default value: `fact`)

> voxel_dimsa list of from 3 to 3 items which are a float

>   Voxel dimensions in mm. Maps to a command-line argument: `-voxeldims %s`.

> trackeda pathlike object or string representing an existing file

>   Output file containing reconstructed tracts.