qsirecon.workflows.recon.dipy module

Dipy Reconstruction workflows

qsirecon.workflows.recon.dipy.init_dipy_brainsuite_shore_recon_wf(inputs_dict, name='dipy_3dshore_recon', qsirecon_suffix='', params={})

Reconstruct EAPs, ODFs, using 3dSHORE (brainsuite-style basis set).

Inputs

qsirecon outputs

Outputs

shore_coeffs

3dSHORE coefficients

rtop

Voxelwise Return-to-origin probability.

rtap

Voxelwise Return-to-axis probability.

rtpp

Voxelwise Return-to-plane probability.

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

convert_to_multishell: str

either “HCP”, “ABCD”, “lifespan” will resample the data with this scheme

radial_order: int

Radial order for spherical harmonics (even)

zeta: float

Zeta parameter for basis set.

tau:float

Diffusion parameter (default= 4 * np.pi**2)

regularization

“L2” or “L1”. Default is “L2”

lambdaN

LambdaN parameter for L2 regularization. (default=1e-8)

lambdaL

LambdaL parameter for L2 regularization. (default=1e-8)

regularization_weighting: int or “CV”

L1 regualrization weighting. Default “CV” (use cross-validation). Can specify a static value to use in all voxels.

l1_positive_constraint: bool

Use positivity constraint.

l1_maxiter

Maximum number of iterations for L1 optization. (Default=1000)

l1_alpha

Alpha parameter for L1 optimization. (default=1.0)

pos_grid: int

Grid points for estimating EAP(default=11)

pos_radius

Radius for EAP estimation (default=20e-03)

qsirecon.workflows.recon.dipy.init_dipy_mapmri_recon_wf(inputs_dict, name='dipy_mapmri_recon', qsirecon_suffix='', params={})

Reconstruct EAPs, ODFs, using 3dSHORE (brainsuite-style basis set).

Inputs

qsirecon outputs

Outputs

shore_coeffs

3dSHORE coefficients

rtop

Voxelwise Return-to-origin probability.

rtap

Voxelwise Return-to-axis probability.

rtpp

Voxelwise Return-to-plane probability.

msd

Voxelwise MSD

qiv

q-space inverse variance

lapnorm

Voxelwise norm of the Laplacian

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

radial_order: int

An even integer that represent the order of the basis

laplacian_regularization: bool

Regularize using the Laplacian of the MAP-MRI basis.

laplacian_weighting: str or scalar

The string ‘GCV’ makes it use generalized cross-validation to find the regularization weight. A scalar sets the regularization weight to that value and an array will make it selected the optimal weight from the values in the array.

positivity_constraint: bool

Constrain the propagator to be positive.

pos_grid: int

Grid points for estimating EAP(default=15)

pos_radius

Radius for EAP estimation (default=20e-03) or “adaptive”

anisotropic_scalingbool,

If True, uses the standard anisotropic MAP-MRI basis. If False, uses the isotropic MAP-MRI basis (equal to 3D-SHORE).

eigenvalue_thresholdfloat,

Sets the minimum of the tensor eigenvalues in order to avoid stability problem.

bval_thresholdfloat,

Sets the b-value threshold to be used in the scale factor estimation. In order for the estimated non-Gaussianity to have meaning this value should set to a lower value (b<2000 s/mm^2) such that the scale factors are estimated on signal points that reasonably represent the spins at Gaussian diffusion.

dti_scale_estimationbool,

Whether or not DTI fitting is used to estimate the isotropic scale factor for isotropic MAP-MRI. When set to False the algorithm presets the isotropic tissue diffusivity to static_diffusivity. This vastly increases fitting speed but at the cost of slightly reduced fitting quality. Can still be used in combination with regularization and constraints.

static_diffusivityfloat,

the tissue diffusivity that is used when dti_scale_estimation is set to False. The default is that of typical white matter D=0.7e-3 _[5].

cvxpy_solverstr, optional

cvxpy solver name. Optionally optimize the positivity constraint with a particular cvxpy solver. See http://www.cvxpy.org/ for details. Default: None (cvxpy chooses its own solver)

qsirecon.workflows.recon.dipy.init_dipy_dki_recon_wf(inputs_dict, name='dipy_dki_recon', qsirecon_suffix='', params={})

Fit DKI

Inputs

qsirecon outputs

Outputs

tensor fa md rd ad color_fa kfa mk ak rk mkt awf

Only if wmti is True

rde

Only if wmti is True

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

radial_order: int

An even integer that represent the order of the basis

qsirecon.workflows.recon.dipy.external_format_datasinks(qsirecon_suffix, params, wf)

Add datasinks for Dipy Reconstructions in other formats.

qsirecon.workflows.recon.dipy.init_dipy_brainsuite_shore_recon_wf(inputs_dict, name='dipy_3dshore_recon', qsirecon_suffix='', params={})

Reconstruct EAPs, ODFs, using 3dSHORE (brainsuite-style basis set).

Inputs

qsirecon outputs

Outputs

shore_coeffs

3dSHORE coefficients

rtop

Voxelwise Return-to-origin probability.

rtap

Voxelwise Return-to-axis probability.

rtpp

Voxelwise Return-to-plane probability.

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

convert_to_multishell: str

either “HCP”, “ABCD”, “lifespan” will resample the data with this scheme

radial_order: int

Radial order for spherical harmonics (even)

zeta: float

Zeta parameter for basis set.

tau:float

Diffusion parameter (default= 4 * np.pi**2)

regularization

“L2” or “L1”. Default is “L2”

lambdaN

LambdaN parameter for L2 regularization. (default=1e-8)

lambdaL

LambdaL parameter for L2 regularization. (default=1e-8)

regularization_weighting: int or “CV”

L1 regualrization weighting. Default “CV” (use cross-validation). Can specify a static value to use in all voxels.

l1_positive_constraint: bool

Use positivity constraint.

l1_maxiter

Maximum number of iterations for L1 optization. (Default=1000)

l1_alpha

Alpha parameter for L1 optimization. (default=1.0)

pos_grid: int

Grid points for estimating EAP(default=11)

pos_radius

Radius for EAP estimation (default=20e-03)

qsirecon.workflows.recon.dipy.init_dipy_dki_recon_wf(inputs_dict, name='dipy_dki_recon', qsirecon_suffix='', params={})

Fit DKI

Inputs

qsirecon outputs

Outputs

tensor fa md rd ad color_fa kfa mk ak rk mkt awf

Only if wmti is True

rde

Only if wmti is True

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

radial_order: int

An even integer that represent the order of the basis

qsirecon.workflows.recon.dipy.init_dipy_mapmri_recon_wf(inputs_dict, name='dipy_mapmri_recon', qsirecon_suffix='', params={})

Reconstruct EAPs, ODFs, using 3dSHORE (brainsuite-style basis set).

Inputs

qsirecon outputs

Outputs

shore_coeffs

3dSHORE coefficients

rtop

Voxelwise Return-to-origin probability.

rtap

Voxelwise Return-to-axis probability.

rtpp

Voxelwise Return-to-plane probability.

msd

Voxelwise MSD

qiv

q-space inverse variance

lapnorm

Voxelwise norm of the Laplacian

Params

write_fibgz: bool

True writes out a DSI Studio fib file

write_mif: bool

True writes out a MRTrix mif file with sh coefficients

radial_order: int

An even integer that represent the order of the basis

laplacian_regularization: bool

Regularize using the Laplacian of the MAP-MRI basis.

laplacian_weighting: str or scalar

The string ‘GCV’ makes it use generalized cross-validation to find the regularization weight. A scalar sets the regularization weight to that value and an array will make it selected the optimal weight from the values in the array.

positivity_constraint: bool

Constrain the propagator to be positive.

pos_grid: int

Grid points for estimating EAP(default=15)

pos_radius

Radius for EAP estimation (default=20e-03) or “adaptive”

anisotropic_scalingbool,

If True, uses the standard anisotropic MAP-MRI basis. If False, uses the isotropic MAP-MRI basis (equal to 3D-SHORE).

eigenvalue_thresholdfloat,

Sets the minimum of the tensor eigenvalues in order to avoid stability problem.

bval_thresholdfloat,

Sets the b-value threshold to be used in the scale factor estimation. In order for the estimated non-Gaussianity to have meaning this value should set to a lower value (b<2000 s/mm^2) such that the scale factors are estimated on signal points that reasonably represent the spins at Gaussian diffusion.

dti_scale_estimationbool,

Whether or not DTI fitting is used to estimate the isotropic scale factor for isotropic MAP-MRI. When set to False the algorithm presets the isotropic tissue diffusivity to static_diffusivity. This vastly increases fitting speed but at the cost of slightly reduced fitting quality. Can still be used in combination with regularization and constraints.

static_diffusivityfloat,

the tissue diffusivity that is used when dti_scale_estimation is set to False. The default is that of typical white matter D=0.7e-3 _[5].

cvxpy_solverstr, optional

cvxpy solver name. Optionally optimize the positivity constraint with a particular cvxpy solver. See http://www.cvxpy.org/ for details. Default: None (cvxpy chooses its own solver)