tractoflow

This subworkflow implements the TractoFlow [1] pipeline. It can process raw diffusion and T1 weighted image to reduce acquisition biases, align anatomical and diffusion space, compute DTI and fODF metrics and generate whole brain tractograms.

---------- Configuration ----------

- nextflow.config : contains an example configuration for the subworkflow. Imports modules.config.
- modules.config : contains the bindings to the modules parameters and some defaults making it Tractoflow.

-------------- Steps --------------

1. PREPROCESS DWI (preproc_dwi, nf-neuro)
Preprocess the DWI image including brain extraction, MP-PCA denoising, eddy current and motion correction, N4 bias correction, normalization and resampling.
2. PREPROCESS T1 (preproc_t1, nf-neuro)
Preprocess the T1 image including brain extraction, NL-Means denoising, bias field correction and resampling.
3. T1 REGISTRATION (anatomical_registration, nf-neuro)
Register the T1 image to the DWI image, using the b0 and the FA map as target for the diffusion space.
4. SEGMENTATION (anatomical_segmentation, nf-neuro)
Segment the T1 image into white matter, gray matter and CSF, in diffusion space.
5. DTI FITTING (dipy)
Fit the diffusion tensor model on the preprocessed DWI image and extract relevant metrics.
6. FRF ESTIMATION (scilpy)
Estimate the Fiber Response Function (FRF) from the preprocessed DWI image.
7. FODF FITTING (dipy)
Fit the Fiber Orientation Distribution Function (fODF), using Single or Multi Shell, Single or Multi Tissues models, on the preprocessed DWI image and extract relevant metrics.
8. PFT TRACKING (dipy)
Perform Particle Filtering Tractography (PFT) on the FODF to generate whole brain tractograms.
9. LOCAL TRACKING (dipy on CPU, scilpy on GPU)
Perform Local Tracking on the FODF to generate whole brain tractograms.

[1] https://tractoflow-documentation.readthedocs.io

Keywords : diffusion, MRI, end-to-end, tractography, preprocessing, fodf, dti

Components : anatomical_segmentation, preproc_dwi, preproc_t1, registration, reconst/dtimetrics, reconst/frf, reconst/fodf, reconst/qball, reconst/meanfrf, registration/antsapplytransforms, tracking/localtracking, tracking/pfttracking, utils_options

Inputs

	Type	Description	Mandatory
ch_dwi	file	The input channel containing the DWI file, B-values and B-vectors in FSL format files. Structure : `tuple val(meta), path(dwi), path(bval), path(bvec)` meta [map] Metadata map. dwi [file] DWI file. bval [file] B-values file. bvec [file] B-vectors file.	True
ch_t1	file	The input channel containing the anatomical T1 weighted image. Structure : `tuple val(meta), path(t1)` meta [map] Metadata map. t1 [file] T1-weighted image file.	True
ch_sbref	file	The input channel containing the single-band b0 reference for the DWI. Structure : `tuple val(meta), path(rev_b0)` meta [map] Metadata map. rev_b0 [file] Single-band b0 reference file.	False
ch_rev_dwi	file	The input channel containing the reverse DWI file, B-values and B-vectors in FSL format files. Structure : `tuple val(meta), path(rev_dwi), path(bval), path(bvec)` meta [map] Metadata map. rev_dwi [file] Reverse-encoded DWI file. bval [file] B-values file. bvec [file] B-vectors file.	False
ch_rev_sbref	file	The input channel containing the reverse b0 file. Structure : `tuple val(meta), path(rev_b0)` meta [map] Metadata map. rev_b0 [file] Reverse b0 file.	False
ch_aparc_aseg	file	The input channel containing freesurfer brain segmentation and gray matter parcellation (aparc+aseg). Must be supplied with ch_wm_parc. When supplied, those are used to generate tissues masks and probability maps. Structure : `tuple val(meta), path(aparc_aseg)` meta [map] Metadata map. aparc_aseg [file] FreeSurfer aparc+aseg parcellation file.	False
ch_wm_parc	file	The input channel containing freesurfer white matter parcellations (wmparc). Must be supplied with ch_aparc_aseg. When supplied, those are used to generate tissues masks and probability maps. Structure : `tuple val(meta), path(wmparc)` meta [map] Metadata map. wmparc [file] FreeSurfer white matter parcellation file.	False
ch_topup_config	file	The input channel containing the config file for Topup. This input is optional. See https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/TopupUsersGuide#Configuration_files. Structure : `tuple path(config_file)` config_file [file] Topup configuration file.	False
ch_bet_template	file	The input channel containing the anatomical template for antsBET. Structure : `tuple val(meta), path(bet_template)` meta [map] Metadata map. bet_template [file] ANTs BET template file.	False
ch_bet_probability_map	file	The input channel containing the brain probability mask for antsBET, with intensity range 1 (definitely brain) to 0 (definitely background). Structure : `tuple val(meta), path(probability_map)` meta [map] Metadata map. probability_map [file] Brain probability mask file.	False
ch_synthstrip_weights	file	The input channel containing the SynthStrip model weights for brain extraction. Structure : `tuple val(meta), path(synthstrip_weights)` meta [map] Metadata map (optional). synthstrip_weights [file] SynthStrip model weights file.	False
ch_lesion_mask	file	The input channel containing the lesion mask for segmentation. Structure : `tuple val(meta), path(lesion_mask)` meta [map] Metadata map. lesion_mask [file] Lesion mask file.	False
options	map	Map of options for the subworkflow. preproc_dwi_run_denoising (boolean) [Default: True] If ‘true’, the DWI will be denoised using the MPPCA method from MRtrix3. preproc_dwi_run_degibbs (boolean) [Default: True] If ‘true’, the Gibbs artifact removal will be performed using the mrdegibbs method from MRtrix3. topup_eddy_run_topup (boolean) [Default: True] If ‘true’, the Topup correction will be performed using FSL’s Topup. topup_eddy_run_eddy (boolean) [Default: True] If ‘true’, the Eddy correction will be performed using FSL’s Eddy. preproc_dwi_run_synthstrip (boolean) [Default: False] If ‘true’, the brain extraction will be performed using SynthStrip. If ‘false’, FSL’s BET will be used. preproc_dwi_keep_dwi_with_skull (boolean) [Default: False] If ‘true’, the DWI before brain extraction will be kept and used for the following steps. If ‘false’, the DWI after brain extraction will be used for the following steps. preproc_dwi_run_N4 (boolean) [Default: True] If ‘true’, the N4 bias field correction will be performed using ANTs’ N4BiasFieldCorrection. preproc_dwi_run_normalize (boolean) [Default: True] If ‘true’, the DWI will be normalized to have a mean value in the WM of approximately 1000 using MRtrix3’s dwinormalise. preproc_dwi_run_resampling (boolean) [Default: True] If ‘true’, the DWI will be resampled to 1 mm isotropic spatial resolution using DIPY’s resample. preproc_t1_run_denoising (boolean) [Default: True] If ‘true’, the anatomical image will be denoised using the nlmeans method from scilpy. preproc_t1_run_N4 (boolean) [Default: True] If ‘true’, the N4 bias field correction will be performed using the N4BiasFieldCorrection method from ANTs. preproc_t1_run_resample (boolean) [Default: True] If ‘true’, the anatomical image will be resampled using the resample method from DIPY. preproc_t1_run_synthstrip (boolean) [Default: False] If ‘true’, the subworkflow will use SynthStrip for brain extraction. preproc_t1_run_ants_bet (boolean) [Default: True] If ‘true’, the subworkflow will use ANTs’ BET for brain extraction. preproc_t1_run_crop (boolean) [Default: True] If ‘true’, the subworkflow will crop the image after brain extraction. frf_average_from_data (boolean) [Default: False] If ‘true’, the fiber response function will be averaged across all subjects. If ‘false’, the fiber response function will be estimated for each subject independently. run_qball (boolean) [Default: False] If ‘true’, the Q-Ball model will be fitted on the DWI data and used for fODF reconstruction and tracking. use_qball_for_tracking (boolean) [Default: False] If ‘true’, the Q-Ball model will be used for tractography instead of the fODF model. This option is only relevant if run_qball is ‘true’. run_pft_tracking (boolean) [Default: True] If ‘true’, Particle Filtering Tractography (PFT) will be performed using DIPY. run_local_tracking (boolean) [Default: False] If ‘true’, Local Tracking will be performed.	True

Outputs

	Type	Description	Mandatory	Pattern
dwi	file	Preprocessed DWI image. Structure : `tuple val(meta), path(dwi), path(bval), path(bvec)` meta [map] Metadata map. dwi [file] Preprocessed DWI file. bval [file] B-values file. bvec [file] B-vectors file.	True
t1	file	T1 image warped to the DWI space. Structure : `tuple val(meta), path(t1)` meta [map] Metadata map. t1 [file] T1 image in DWI space.	True
wm_mask	file	White matter mask. Structure : `tuple val(meta), path(wm_mask)` meta [map] Metadata map. wm_mask [file] White matter mask file.	True
gm_mask	file	Gray matter mask. Structure : `tuple val(meta), path(gm_mask)` meta [map] Metadata map. gm_mask [file] Gray matter mask file.	True
csf_mask	file	Cerebrospinal fluid mask. Structure : `tuple val(meta), path(csf_mask)` meta [map] Metadata map. csf_mask [file] CSF mask file.	True
wm_map	file	White matter probability map. Structure : `tuple val(meta), path(wm_map)` meta [map] Metadata map. wm_map [file] White matter probability map file.	True
gm_map	file	Gray matter probability map. Structure : `tuple val(meta), path(gm_map)` meta [map] Metadata map. gm_map [file] Gray matter probability map file.	True
csf_map	file	Cerebrospinal fluid probability map. Structure : `tuple val(meta), path(csf_map)` meta [map] Metadata map. csf_map [file] CSF probability map file.	True
aparc_aseg	file	Freesurfer brain segmentation and gray matter parcellation (aparc+aseg) in diffusion space. Only available if ch_aparc_aseg is provided in inputs. Structure : `tuple val(meta), path(aparc_aseg)` meta [map] Metadata map. aparc_aseg [file] FreeSurfer aparc+aseg in diffusion space.	False
wmparc	file	Freesurfer white matter parcellations (wmparc) in diffusion space. Only available if ch_wm_parc is provided in inputs. Structure : `tuple val(meta), path(wmparc)` meta [map] Metadata map. wmparc [file] FreeSurfer wmparc in diffusion space.	False
anatomical_to_diffusion	file	Transformation matrix from the anatomical space to the diffusion space. Structure : `tuple val(meta), path(warp), path(affine)` meta [map] Metadata map. warp [file] Warp field file. affine [file] Affine transformation file.	True
diffusion_to_anatomical	file	Transformation matrix from the diffusion space to the anatomical space. Structure : `tuple val(meta), path(affine), path(warp)` meta [map] Metadata map. affine [file] Affine transformation file. warp [file] Warp field file.	True
t1_native	file	Preprocessed T1 in anatomical space. Structure : `tuple val(meta), path(t1)` meta [map] Metadata map. t1 [file] T1 in native anatomical space.	True
dti_tensor	file	4-D Diffusion tensor image, with 6 components in the last dimensions, ordered by FSL convention (row-major : Dxx, Dxy, Dxz, Dyy, Dyz, Dzz). Structure : `tuple val(meta), path(dti_tensor)` meta [map] Metadata map. dti_tensor [file] DTI tensor file.	True
dti_md	file	Mean diffusivity map. Structure : `tuple val(meta), path(dti_md)` meta [map] Metadata map. dti_md [file] Mean diffusivity file.	True
dti_rd	file	Radial diffusivity map. Structure : `tuple val(meta), path(dti_rd)` meta [map] Metadata map. dti_rd [file] Radial diffusivity file.	True
dti_ad	file	Axial diffusivity map. Structure : `tuple val(meta), path(dti_ad)` meta [map] Metadata map. dti_ad [file] Axial diffusivity file.	True
dti_fa	file	Fractional anisotropy map. Structure : `tuple val(meta), path(dti_fa)` meta [map] Metadata map. dti_fa [file] Fractional anisotropy file.	True
dti_rgb	file	RGB map of the diffusion tensor. Structure : `tuple val(meta), path(dti_rgb)` meta [map] Metadata map. dti_rgb [file] DTI RGB file.	True
dti_peaks	file	Principal direction of the diffusion tensor. Structure : `tuple val(meta), path(dti_peaks)` meta [map] Metadata map. dti_peaks [file] DTI peaks file.	True
dti_evecs	file	Eigenvectors of the diffusion tensor, ordered by eigenvalue. Structure : `tuple val(meta), path(dti_evecs)` meta [map] Metadata map. dti_evecs [file] DTI eigenvectors file.	True
dti_evals	file	Eigenvalues of the diffusion tensor. Structure : `tuple val(meta), path(dti_evals)` meta [map] Metadata map. dti_evals [file] DTI eigenvalues file.	True
dti_residual	file	Residuals of the diffusion tensor fitting. Structure : `tuple val(meta), path(dti_residual)` meta [map] Metadata map. dti_residual [file] DTI residual file.	True
dti_ga	file	Generalized anisotropy map. Structure : `tuple val(meta), path(dti_ga)` meta [map] Metadata map. dti_ga [file] Generalized anisotropy file.	True
dti_mode	file	Mode of the diffusion tensor. Structure : `tuple val(meta), path(dti_mode)` meta [map] Metadata map. dti_mode [file] DTI mode file.	True
dti_norm	file	Norm of the diffusion tensor. Structure : `tuple val(meta), path(dti_norm)` meta [map] Metadata map. dti_norm [file] DTI norm file.	True
fiber_response	file	Fiber Response Function (FRF) estimated from the DWI image. If using Single Tissue: contains fiber_response file. If using Multi Tissues: contains wm_fiber_response, gm_fiber_response, and csf_fiber_response files. Structure : `tuple val(meta), path(fiber_response), path(wm_fiber_response), path(gm_fiber_response), path(csf_fiber_response)` meta [map] Metadata map. fiber_response [file] Single-tissue FRF file (when not using multi-tissue). wm_fiber_response [file] White matter FRF file (multi-tissue). gm_fiber_response [file] Gray matter FRF file (multi-tissue). csf_fiber_response [file] CSF FRF file (multi-tissue).	True
fodf	file	Fiber Orientation Distribution Function (fODF) estimated from the DWI image. If using Single Tissue: contains fodf file. If using Multi Tissues: contains wm_fodf, gm_fodf, and csf_fodf files. Structure : `tuple val(meta), path(fodf), path(wm_fodf), path(gm_fodf), path(csf_fodf)` meta [map] Metadata map. fodf [file] Single-tissue fODF file (when not using multi-tissue). wm_fodf [file] White matter fODF file (multi-tissue). gm_fodf [file] Gray matter fODF file (multi-tissue). csf_fodf [file] CSF fODF file (multi-tissue).	True
fodf_rgb	file	RGB map of the fODF, normalized by volume fraction of WM. Structure : `tuple val(meta), path(fodf_rgb)` meta [map] Metadata map. fodf_rgb [file] fODF RGB file.	True
fodf_peaks	file	Peaks of the fODF. Structure : `tuple val(meta), path(fodf_peaks)` meta [map] Metadata map. fodf_peaks [file] fODF peaks file.	True
afd_max	file	Maximum Apparent Fiber Density (AFD) map. Structure : `tuple val(meta), path(afd_max)` meta [map] Metadata map. afd_max [file] AFD max file.	True
afd_total	file	Total Apparent Fiber Density (AFD) map. Structure : `tuple val(meta), path(afd_total)` meta [map] Metadata map. afd_total [file] AFD total file.	True
afd_sum	file	Sum of Apparent Fiber Density (AFD) map. Structure : `tuple val(meta), path(afd_sum)` meta [map] Metadata map. afd_sum [file] AFD sum file.	True
nufo	file	Number of Unique Fibers Orientations (NUFO) map. Structure : `tuple val(meta), path(nufo)` meta [map] Metadata map. nufo [file] NUFO file.	True
volume_fraction	file	Tissues volume fraction map. Structure : `tuple val(meta), path(volume_fraction)` meta [map] Metadata map. volume_fraction [file] Tissues volume fraction file.	True
qball	file	Qball spherical harmonics coefficients. Structure : `tuple val(meta), path(qball)` meta [map] Metadata map. qball [file] Qball SH coefficients file.	False
qball_a_power	file	Anisotropic power map. Structure : `tuple val(meta), path(qball_a_power)` meta [map] Metadata map. qball_a_power [file] Qball anisotropic power file.	False
qball_peaks	file	Extracted peaks from Qball model. Structure : `tuple val(meta), path(qball_peaks)` meta [map] Metadata map. qball_peaks [file] Qball peaks file.	False
qball_peaks_indices	file	Generated peaks indices on the sphere from Qball model. Structure : `tuple val(meta), path(qball_peak_indices)` meta [map] Metadata map. qball_peak_indices [file] Qball peak indices file.	False
qball_gfa	file	Generalized fractional anisotropy. Structure : `tuple val(meta), path(qball_gfa)` meta [map] Metadata map. qball_gfa [file] Qball GFA file.	False
qball_nufo	file	NUFO map from Qball model. Structure : `tuple val(meta), path(qball_nufo)` meta [map] Metadata map. qball_nufo [file] Qball NUFO file.	False
pft_tractogram	file	Whole brain tractogram generated with Particle Filtering Tractography (PFT). Structure : `tuple val(meta), path(pft_tractogram)` meta [map] Metadata map. pft_tractogram [file] PFT tractogram file.	False
pft_config	file	Configuration file used for Particle Filtering Tractography (PFT). Structure : `tuple val(meta), path(pft_config)` meta [map] Metadata map. pft_config [file] PFT configuration file.	False
pft_map_include	file	Include map used for Particle Filtering Tractography (PFT). Structure : `tuple val(meta), path(pft_map_include)` meta [map] Metadata map. pft_map_include [file] PFT include map file.	False
pft_map_exclude	file	Exclude map used for Particle Filtering Tractography (PFT). Structure : `tuple val(meta), path(pft_map_exclude)` meta [map] Metadata map. pft_map_exclude [file] PFT exclude map file.	False
pft_seeding_mask	file	Seeding mask used for Particle Filtering Tractography (PFT). Structure : `tuple val(meta), path(pft_seeding_mask)` meta [map] Metadata map. pft_seeding_mask [file] PFT seeding mask file.	False
local_tractogram	file	Whole brain tractogram generated with Local Tracking. Structure : `tuple val(meta), path(local_tractogram)` meta [map] Metadata map. local_tractogram [file] Local tractogram file.	False
local_config	file	Configuration file used for Local Tracking. Structure : `tuple val(meta), path(local_config)` meta [map] Metadata map. local_config [file] Local tracking configuration file.	False
local_seeding_mask	file	Seeding mask used for Local Tracking. Structure : `tuple val(meta), path(local_seeding_mask)` meta [map] Metadata map. local_seeding_mask [file] Local tracking seeding mask file.	False
local_tracking_mask	file	Tracking mask used for Local Tracking. Structure : `tuple val(meta), path(local_tracking_mask)` meta [map] Metadata map. local_tracking_mask [file] Local tracking mask file.	False
mqc	file	QC files (png, gif, etc.) generated by all modules that can be leveraged in a subject-level MultiQC report. Structure : `tuple val(meta), path(mqc_files)` meta [map] Metadata map. mqc_files [file] MultiQC files.	False
global_mqc	file	QC files (txt, npy, etc.) generated by all modules that can be leveraged in a global-level MultiQC report (all subjects). Structure : `tuple val(meta), path(global_mqc_files)` meta [map] Metadata map. global_mqc_files [file] Global MultiQC files.	False
versions	file	File containing software versions Structure : `tuple path(versions)` versions [file] Versions YAML file.	True	`versions.yml`

Parameters (see configuration)

	Type	Description	Default	Choices
b0_max_threshold	float	{‘type’: ‘float’, ‘description’: ‘Maximum b-value threshold to consider as b0 volumes.’, ‘default’: 10}	10
bvalue_tolerance	float	{‘type’: ‘float’, ‘description’: ‘Tolerance for b-value shell extraction and processing.’, ‘default’: 20}	20
dwi_signal_sh_fit	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable spherical harmonics fitting of the DWI signal.’, ‘default’: True}	True
dwi_signal_sh_fit_order	integer	{‘type’: ‘integer’, ‘description’: ‘Spherical harmonics order for DWI signal fitting.’, ‘default’: 6}	6
dwi_signal_sh_fit_basis	string	{‘type’: ‘string’, ‘description’: ‘Spherical harmonics basis for DWI signal fitting.’, ‘choices’: [‘descoteaux07’, ‘descoteaux07_legacy’, ‘tournier07’, ‘tournier07_legacy’], ‘default’: ‘descoteaux07’}	descoteaux07	- descoteaux07 - descoteaux07_legacy - tournier07 - tournier07_legacy
dwi_signal_sh_fit_shell	boolean	{‘type’: ‘boolean’, ‘description’: ‘Fit spherical harmonics on specific shell instead of all shells.’, ‘default’: False}	False
dwi_denoise_patch_size	integer	{‘type’: ‘integer’, ‘description’: ‘Patch size for MP-PCA denoising algorithm.’, ‘default’: 7}	7
dwi_topup_config_file	string	{‘type’: ‘string’, ‘description’: ‘Configuration file for TOPUP processing (e.g., “b02b0.cnf”).’, ‘default’: ‘b02b0.cnf’}	b02b0.cnf
dwi_eddy_executable	string	{‘type’: ‘string’, ‘description’: ‘EDDY executable to use (e.g., “eddy_cpu” or “eddy_cuda10.2”).’, ‘choices’: [‘eddy_cpu’, ‘eddy_cuda10.2’], ‘default’: ‘eddy_cpu’}	eddy_cpu	- eddy_cpu - eddy_cuda10.2
dwi_eddy_restore_slices	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable slice-wise outlier detection and replacement in EDDY.’, ‘default’: True}	True
preproc_t1_run_denoising	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable NL-Means denoising of the T1 image.’, ‘default’: True}	True
preproc_t1_run_N4	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable N4 bias field correction for T1 image.’, ‘default’: True}	True
preproc_t1_run_synthstrip	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable SynthStrip brain extraction for T1 image.’, ‘default’: False}	False
preproc_t1_run_ants_bet	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable ANTs-based brain extraction for T1 image.’, ‘default’: True}	True
preproc_t1_run_crop	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable cropping of the T1 image to remove padding.’, ‘default’: True}	True
preproc_t1_run_resampling	boolean	{‘type’: ‘boolean’, ‘description’: ‘Enable resampling of the T1 image to isotropic resolution.’, ‘default’: True}	True
t1_resample_resolution_mm_iso	integer	{‘type’: ‘integer’, ‘description’: ‘Isotropic resolution in mm for T1 resampling.’, ‘default’: 1}	1
t1_resample_interpolation	string	{‘type’: ‘string’, ‘description’: ‘Interpolation method for T1 resampling (e.g., “lin”, “cubic”).’, ‘choices’: [‘nn’, ‘lin’, ‘quad’, ‘cubic’], ‘default’: ‘lin’}	lin	- nn - lin - quad - cubic
dwi_n4_knot_interval	integer	{‘type’: ‘integer’, ‘description’: “Number of voxels between splines’ knots for DWI N4 bias correction.”, ‘default’: 1}	1
dwi_n4_subsampling	integer	{‘type’: ‘integer’, ‘description’: ‘Subsampling factor for DWI N4 bias correction.’, ‘default’: 2}	2
dwi_resample_resolution_mm_iso	integer	{‘type’: ‘integer’, ‘description’: ‘Isotropic resolution in mm for DWI resampling.’, ‘default’: 1}	1
dwi_resample_interpolation	string	{‘type’: ‘string’, ‘description’: ‘Interpolation method for DWI resampling (e.g., “lin”, “cubic”).’, ‘choices’: [‘nn’, ‘lin’, ‘quad’, ‘cubic’], ‘default’: ‘lin’}	lin	- nn - lin - quad - cubic
dti_max_bvalue	integer	{‘type’: ‘integer’, ‘description’: ‘Maximum b-value to consider for DTI shell extraction.’, ‘default’: 1200}	1200
dti_shells_to_fit	string	{‘type’: ‘string’, ‘description’: ‘Specific shells to use for DTI fitting (e.g., “0,1000” for b-values).’, ‘default’: None}	None
fodf_min_bvalue	integer	{‘type’: ‘integer’, ‘description’: ‘Minimum b-value to consider for fODF shell extraction.’, ‘default’: 700}	700
fodf_shells_to_fit	string	{‘type’: ‘string’, ‘description’: ‘Specific shells to use for fODF fitting (e.g., “0,1000,2000” for b-values).’, ‘default’: None}	None
frf_fa_max_threshold	float	{‘type’: ‘float’, ‘description’: ‘Maximum FA threshold for fiber response function estimation.’, ‘default’: 0.7}	0.7
frf_fa_min_threshold	float	{‘type’: ‘float’, ‘description’: ‘Minimum FA threshold for fiber response function estimation.’, ‘default’: 0.5}	0.5
frf_min_n_voxels	integer	{‘type’: ‘integer’, ‘description’: ‘Minimum number of voxels required for FRF estimation.’, ‘default’: 300}	300
frf_roi_radius	integer	{‘type’: ‘integer’, ‘description’: ‘ROI radius in mm for fiber response function estimation.’, ‘default’: 20}	20
frf_value_to_force	string	{‘type’: ‘string’, ‘description’: ‘Force specific FRF values instead of estimation (e.g., “15,4,4” for eigenvalues).’, ‘default’: None}	None
fodf_sh_order	integer	{‘type’: ‘integer’, ‘description’: ‘Spherical harmonics order for fODF reconstruction (must be even).’, ‘default’: 8}	8
fodf_sh_basis	string	{‘type’: ‘string’, ‘description’: ‘Spherical harmonics basis for fODF reconstruction.’, ‘choices’: [‘descoteaux07’, ‘descoteaux07_legacy’, ‘tournier07’, ‘tournier07_legacy’], ‘default’: ‘descoteaux07’}	descoteaux07	- descoteaux07 - descoteaux07_legacy - tournier07 - tournier07_legacy
fodf_peaks_absolute_factor	float	{‘type’: ‘float’, ‘description’: ‘Absolute threshold factor for fODF peak extraction.’, ‘default’: 2.0}	2.0
fodf_peaks_relative_threshold	float	{‘type’: ‘float’, ‘description’: ‘Relative threshold for fODF peak extraction.’, ‘default’: 0.1}	0.1
fodf_peaks_ventricle_max_fa	float	{‘type’: ‘float’, ‘description’: ‘Maximum FA value for ventricle mask creation.’, ‘default’: 0.1}	0.1
fodf_peaks_ventricle_min_md	float	{‘type’: ‘float’, ‘description’: ‘Minimum MD value for ventricle mask creation.’, ‘default’: 0.003}	0.003
pft_random_seed	integer	{‘type’: ‘integer’, ‘description’: ‘Random seed for PFT tractography reproducibility.’, ‘default’: 0}	0
pft_algorithm	string	{‘type’: ‘string’, ‘description’: ‘Algorithm to use for PFT tractography (e.g., “prob”, “det”).’, ‘choices’: [‘prob’, ‘det’], ‘default’: ‘prob’}	prob	- prob - det
pft_step_mm	float	{‘type’: ‘float’, ‘description’: ‘Step size in mm for PFT tractography.’, ‘default’: 0.5}	0.5
pft_theta_max_deviation	integer	{‘type’: ‘integer’, ‘description’: ‘Maximum angular deviation in degrees for PFT tracking.’, ‘default’: 20}	20
pft_min_streamline_length	integer	{‘type’: ‘integer’, ‘description’: ‘Minimum streamline length in mm for PFT tracking.’, ‘default’: 20}	20
pft_max_streamline_length	integer	{‘type’: ‘integer’, ‘description’: ‘Maximum streamline length in mm for PFT tracking.’, ‘default’: 200}	200
pft_seeding_type	string	{‘type’: ‘string’, ‘description’: ‘Tissue type for PFT seeding mask generation (e.g., “wm”, “fa”).’, ‘choices’: [‘wm’, ‘interface’, ‘fa’], ‘default’: ‘wm’}	wm	- wm - interface - fa
pft_seeding_strategy	string	{‘type’: ‘string’, ‘description’: ‘Seeding strategy for PFT tractography (e.g., “npv”, “nt”).’, ‘choices’: [‘npv’, ‘nt’], ‘default’: ‘npv’}	npv	- npv - nt
pft_number_of_seeds	integer	{‘type’: ‘integer’, ‘description’: ‘Number of seeds per voxel - npv (or total - nt) for PFT tracking.’, ‘default’: 10}	10
pft_fa_min_threshold	float	{‘type’: ‘float’, ‘description’: ‘Minimum FA threshold for PFT tracking.’, ‘default’: 0.1}	0.1
pft_number_of_particles	integer	{‘type’: ‘integer’, ‘description’: ‘Number of particles for PFT algorithm.’, ‘default’: 15}	15
pft_backward_step_mm	integer	{‘type’: ‘integer’, ‘description’: ‘Backward step size in mm for PFT tracking.’, ‘default’: 2}	2
pft_forward_step_mm	integer	{‘type’: ‘integer’, ‘description’: ‘Forward step size in mm for PFT tracking.’, ‘default’: 1}	1
pft_compression_step_mm	float	{‘type’: ‘float’, ‘description’: ‘Compression step size in mm for PFT streamlines.’, ‘default’: 0.2}	0.2
lt_processor	string	{‘type’: ‘string’, ‘description’: ‘Processor type for local tracking (e.g., “cpu”, “gpu”).’, ‘choices’: [‘cpu’, ‘gpu’], ‘default’: ‘cpu’}	cpu	- cpu - gpu
lt_gpu_batch_size	integer	{‘type’: ‘integer’, ‘description’: ‘Batch size for GPU-based local tracking.’, ‘default’: 10000}	10000
lt_random_seed	integer	{‘type’: ‘integer’, ‘description’: ‘Random seed for local tracking reproducibility.’, ‘default’: 0}	0
lt_algorithm	string	{‘type’: ‘string’, ‘description’: ‘Algorithm to use for local tractography (e.g., “prob”, “det”).’, ‘choices’: [‘prob’, ‘det’, ‘ptt’, ‘eudx’], ‘default’: ‘prob’}	prob	- prob - det - ptt - eudx
lt_step_mm	float	{‘type’: ‘float’, ‘description’: ‘Step size in mm for local tracking.’, ‘default’: 0.5}	0.5
lt_theta_max_deviation	integer	{‘type’: ‘integer’, ‘description’: ‘Maximum angular deviation in degrees for local tracking.’, ‘default’: 20}	20
lt_min_streamline_length	integer	{‘type’: ‘integer’, ‘description’: ‘Minimum streamline length in mm for local tracking.’, ‘default’: 20}	20
lt_max_streamline_length	integer	{‘type’: ‘integer’, ‘description’: ‘Maximum streamline length in mm for local tracking.’, ‘default’: 200}	200
lt_seeding_type	string	{‘type’: ‘string’, ‘description’: ‘Tissue type for local tracking seeding mask generation (e.g., “wm”, “fa”).’, ‘choices’: [‘wm’, ‘fa’], ‘default’: ‘wm’}	wm	- wm - fa
lt_seeding_strategy	string	{‘type’: ‘string’, ‘description’: ‘Seeding strategy for local tractography (e.g., “npv”, “nt”).’, ‘choices’: [‘npv’, ‘nt’], ‘default’: ‘npv’}	npv	- npv - nt
lt_number_of_seeds	integer	{‘type’: ‘integer’, ‘description’: ‘Number of seeds per voxel for local tracking.’, ‘default’: 10}	10
lt_fa_min_threshold_for_seeding	float	{‘type’: ‘float’, ‘description’: ‘Minimum FA threshold for local tracking seeding mask.’, ‘default’: 0.1}	0.1
lt_tracking_type	string	{‘type’: ‘string’, ‘description’: ‘Tissue type for local tracking mask generation (e.g., “wm”, “fa”).’, ‘choices’: [‘wm’, ‘fa’], ‘default’: ‘wm’}	wm	- wm - fa
lt_fa_min_threshold_for_tracking	float	{‘type’: ‘float’, ‘description’: ‘Minimum FA threshold for local tracking mask.’, ‘default’: 0.1}	0.1
lt_compression_step_mm	float	{‘type’: ‘float’, ‘description’: ‘Compression step size in mm for local tracking streamlines.’, ‘default’: 0.2}	0.2

Authors

@AlexVCaron

Maintainers

@AlexVCaron

Last updated : 2026-03-17