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Multitenfit

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multitenfit

Section: User Commands (1)
Index Return to Main Contents

 

NAME

multitenfit - Fits one or more diffusion tensors to the data in each voxel according to a voxel classification map to specify the number of tensors to fit in each voxel.

 

SYNOPSIS

multitenfit -schemefile <scheme file> -voxclassmap <voxel classification> [-options]

 

DESCRIPTION

Reads diffusion MRI data, acquired using the acquisition scheme detailed in the scheme file, from the data file. The data file stores the diffusion MRI data in voxel order with the measurements stored as big-endian four-byte floats and ordered as in the scheme file. See modelfit(1) for the format of the data file and scheme file. The program also reads a voxel classification map, which contains an integer label for each voxel. The order of the voxels in the classification must be the same as in the input data file.

The program has a list of model identity codes, or "inversion indices" see modelfit(1) for a list; one for each possible class in the voxel classification. If the classification of a voxel is i, the program uses the inversion indexed by the i-th element of the list for the data in that voxel. If the voxel classification is greater than the length of the list, the program uses the inversion indexed by the last element of the list. By default, the list of indices is {1, 1, 1, 1, 31}, so that voxel classifications 0, 1, 2 and 3 all imply fitting a single tensor by linear least-squares to the log measurements and a classification of 4 or above implies inversion 31, which is two positive-definite tensors. The standard output format of voxelclassify(1), which is 0 for isotropic, 2 for anisotropic Gaussian and 4 for non-Gaussian, motivates the default list. With this voxel classification, elements 1 and 3 (counting from zero) of the list are redundant.

The program outputs the results, in voxel order and as big-endian eight-byte doubles, to the standard output. The output format is: [exitcode, ln(S(0)), m, a_1, D_1xx, D_1xy, D_1xz, D_1yy, D_1yz, D_1zz, a_2, D_2xx, D_2xy, D_2xz, D_2yy, D_2yz, D_2zz, ..., a_n, D_nxx, D_nxy, D_nxz, D_nyy, D_nyz, D_nzz], where n is the maximum number of tensors in each voxel (which can be set using the command line option -maxcomponents) and m is the actual number fitted and output in this voxel. If n > m, diffusion tensors above index m contain all zeros and their mixing parameters are zero. 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.

 

EXAMPLES

Classify the voxels using voxelclassify and use the output for fitting multiple tensors in each voxel according to that classification, ie, one tensor where the voxel classification says isotropic or anisotropic Gaussian, two tensors where it says non-Gaussian:


  voxelclassify -schemefile A.scheme -ftest 1.0E-20 1.0E-6 1.0E-6 < SubjectA.Bfloat > A_VC.Bint


  multitenfit -schemefile A.scheme -voxclassmap A_VC.Bint < SubjectA.Bfloat > MultiTensorA.Bdouble

Fit tensors using non-linear fitting in isotropic and anisotropic Gaussian voxels and two cylindrically symmetric positive-definite in non-Gaussian voxels:


  multitenfit -schemefile A.scheme -voxclassmap A_VC.Bint -classifiedmodels 5 2 2 2 2 11 < SubjectA.Bfloat > MultiTensorA.Bdouble

Use a different voxel classification which contains zero in voxels with no clear fibre orientations, one in voxels with one fibre, two in voxels with two fibres and three in voxels with three fibres. With zero or one fibre, just fit the diffusion tensor; with two fibres fit a pair of cylindrically symmetric diffusion tensors; with three fit three cylindrically symmetric tensors:


  multitenfit -schemefile A.scheme -voxclassmap A_VC2.Bint -classifiedmodels 4 2 2 11 211 < SubjectA.Bfloat > MultiTensorA.Bdouble

 

OPTIONS

-schemefile <scheme file>
Scheme file, see camino(1).

-voxclassmap <voxel classification file>
Specify the file containing the voxel classification map.

-classifiedmodels <number of classes> <list of inversion indices>
Specifies the inversion to use for each voxel class in the voxel classification map. For voxels with label i in the voxel classification map, the program uses the inversion with index at position i in the list of inversion indices. The first number after this command line flag specifies the length of the list. The subsequent list of inversion indices must contain that number of elements. The default is {1, 1, 1, 1, 31}, which is compatible with the output of voxelclassify.

The new text model codes (see modelfit(1)) may also be used: {ldt, ldt, ldt, ldt, pospos} is the same as {1, 1, 1, 1, 31}. If a text code is used for a two- or three-tensor model, the single-tensor initialization is the first model in the list, so {1, pospos_eq} is the same as {1, 21} and {2, pospos_eq} is the same as {2, 22}, but {2, 21} must be specified numerically.

-maxcomponents <number of output components>
Specifies the number of components (ie diffusion tensors) to output in each voxel. Normally, this number is the same as the largest number of diffusion tensors fit in any of the inversions in the list of inversion indices. The default is two.

multitenfit also takes other standard command line options listed in modelfit(1).

 

AUTHORS

Daniel Alexander <camino@cs.ucl.ac.uk>

 

SEE ALSO

modelfit(1), dtfit(1), twotenfit(1), threetenfit(1), fa(1), trd(1), shfit(1), shanis(1), voxelclassify(1)

 

BUGS


 

Index

NAME
SYNOPSIS
DESCRIPTION
EXAMPLES
OPTIONS
AUTHORS
SEE ALSO
BUGS

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Time: 02:07:11 GMT, December 04, 2017

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