Module: 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 ----------
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nextflow.config : contains an example configuration for the subworkflow. └── modules.config : contains the bindings to the modules parameters and some defaults making it Tractoflow.
i.e: nextflow.config import modules.config -------------- Steps -------------- 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. PREPROCESS T1 (preproc_t1, nf-neuro) Preprocess the T1 image including brain extraction, NL-Means denoising, bias field correction and resampling. 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. SEGMENTATION (anatomical_segmentation, nf-neuro) Segment the T1 image into white matter, gray matter and CSF, in diffusion space. DTI FITTING (dipy) Fit the diffusion tensor model on the preprocessed DWI image and extract relevant metrics. FRF ESTIMATION (scilpy) Estimate the Fiber Response Function (FRF) from the preprocessed DWI image. 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. PFT TRACKING (dipy) Perform Particle Filtering Tractography (PFT) on the FODF to generate whole brain tractograms. 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
Inputs
| Type | Description | Pattern | |
|---|---|---|---|
| ch_dwi | file | The input channel containing the DWI file, B-values and B-vectors in FSL format files. Structure: [ val(meta), path(dwi), path(bval), path(bvec) ] | |
| ch_t1 | file | The input channel containing the anatomical T1 weighted image. Structure: [ val(meta), path(t1) ] | |
| ch_sbref | file | (Optional) The input channel containing the single-band b0 reference for the DWI. Structure: [ val(meta), path(rev_b0) ] | |
| ch_rev_dwi | file | (Optional) The input channel containing the reverse DWI file, B-values and B-vectors in FSL format files. Structure: [ val(meta), path(rev_dwi), path(bval), path(bvec) ] | |
| ch_rev_sbref | file | (Optional) The input channel containing the reverse b0 file. Structure: [ val(meta), path(rev_b0) ] | |
| ch_aparc_aseg | file | (Optional) 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: [ val(meta), path(aparc_aseg) ] | |
| ch_wm_parc | file | (Optional) 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: [ val(meta), path(wmparc) ] | |
| ch_topup_config | file | (Optional) 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: [ path(config_file) ] | |
| ch_bet_template | file | (Optional) The input channel containing the anatomical template for antsBET. Structure: [ val(meta), path(bet_template) ] | |
| ch_bet_probability_map | file | (Optional) The input channel containing the brain probability mask for antsBET, with intensity range 1 (definitely brain) to 0 (definitely background). Structure: [ val(meta), path(probability_map) ] | |
| ch_lesion_mask | file | (Optional) The input channel containing the lesion mask for segmentation. Structure: [ val(meta), path(lesion_mask) ] |
Outputs
| Type | Description | Pattern | |
|---|---|---|---|
| dwi | file | Preprocessed DWI image. Structure: [ val(meta), path(dwi), path(bval), path(bvec) ] | |
| t1 | file | T1 image warped to the DWI space. Structure: [ val(meta), path(t1) ] | |
| wm_mask | file | White matter mask. Structure: [ val(meta), path(wm_mask) ] | |
| gm_mask | file | Gray matter mask. Structure: [ val(meta), path(gm_mask) ] | |
| csf_mask | file | Cerebrospinal fluid mask. Structure: [ val(meta), path(csf_mask) ] | |
| wm_map | file | White matter probability map. Structure: [ val(meta), path(wm_map) ] | |
| gm_map | file | Gray matter probability map. Structure: [ val(meta), path(gm_map) ] | |
| csf_map | file | Cerebrospinal fluid probability map. Structure: [ val(meta), path(csf_map) ] | |
| aparc_aseg | file | (Optional) Freesurfer brain segmentation and gray matter parcellation (aparc+aseg) in diffusion space. Only available if ch_aparc_aseg is provided in inputs. Structure: [ val(meta), path(aparc_aseg) ] | |
| wmparc | file | (Optional) Freesurfer white matter parcellations (wmparc) in diffusion space. Only available if ch_wm_parc is provided in inputs. Structure: [ val(meta), path(wmparc) ] | |
| anatomical_to_diffusion | file | Transformation matrix from the anatomical space to the diffusion space. Structure: [ val(meta), [<path(warp)>, path(affine)] ] | |
| diffusion_to_anatomical | file | Transformation matrix from the diffusion space to the anatomical space. Structure: [ val(meta), [path(affine), <path(warp)>] ] | |
| t1_native | file | Preprocessed T1 in anatomical space. Structure: [ val(meta), path(t1) ] | |
| 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: [ val(meta), path(dti_tensor) ] | |
| dti_md | file | Mean diffusivity map. Structure: [ val(meta), path(dti_md) ] | |
| dti_rd | file | Radial diffusivity map. Structure: [ val(meta), path(dti_rd) ] | |
| dti_ad | file | Axial diffusivity map. Structure: [ val(meta), path(dti_ad) ] | |
| dti_fa | file | Fractional anisotropy map. Structure: [ val(meta), path(dti_fa) ] | |
| dti_rgb | file | RGB map of the diffusion tensor. Structure: [ val(meta), path(dti_rgb) ] | |
| dti_peaks | file | Principal direction of the diffusion tensor. Structure: [ val(meta), path(dti_peaks) ] | |
| dti_evecs | file | Eigenvectors of the diffusion tensor, ordered by eigenvalue. Structure: [ val(meta), path(dti_evecs) ] | |
| dti_evals | file | Eigenvalues of the diffusion tensor. Structure: [ val(meta), path(dti_evals) ] | |
| dti_residual | file | Residuals of the diffusion tensor fitting. Structure: [ val(meta), path(dti_residual) ] | |
| dti_ga | file | Generalized anisotropy map. Structure: [ val(meta), path(dti_ga) ] | |
| dti_mode | file | Mode of the diffusion tensor. Structure: [ val(meta), path(dti_mode) ] | |
| dti_norm | file | Norm of the diffusion tensor. Structure: [ val(meta), path(dti_norm) ] | |
| fiber_response | file | Fiber Response Function (FRF) estimated from the DWI image. If using Single Tissue Structure: [ val(meta), path(fiber_response) ] If using Multi Tissues Structure: [ val(meta), path(wm_fiber_response), path(gm_fiber_response), path(csf_fiber_response) ] | |
| fodf | file | Fiber Orientation Distribution Function (fODF) estimated from the DWI image. If using Single Tissue Structure: [ val(meta), path(fodf) ] If using Multi Tissues Structure: [ val(meta), path(wm_fodf), path(gm_fodf), path(csf_fodf) ] | |
| fodf_rgb | file | RGB map of the fODF, normalized by volume fraction of WM. Structure: [ val(meta), path(fodf_rgb) ] | |
| fodf_peaks | file | Peaks of the fODF. Structure: [ val(meta), path(fodf_peaks) ] | |
| afd_max | file | Maximum Apparent Fiber Density (AFD) map. Structure: [ val(meta), path(afd_max) ] | |
| afd_total | file | Total Apparent Fiber Density (AFD) map. Structure: [ val(meta), path(afd_total) ] | |
| afd_sum | file | Sum of Apparent Fiber Density (AFD) map. Structure: [ val(meta), path(afd_sum) ] | |
| nufo | file | Number of Unique Fibers Orientations (NUFO) map. Structure: [ val(meta), path(nufo) ] | |
| volume_fraction | file | Tissues volume fraction map. Structure: [ val(meta), path(volume_fraction) ] | |
| pft_tractogram | file | Whole brain tractogram generated with Particle Filtering Tractography (PFT). Structure: [ val(meta), path(pft_tractogram) ] | |
| pft_config | file | Configuration file used for Particle Filtering Tractography (PFT). Structure: [ val(meta), path(pft_config) ] | |
| pft_map_include | file | Include map used for Particle Filtering Tractography (PFT). Structure: [ val(meta), path(pft_map_include) ] | |
| pft_map_exclude | file | Exclude map used for Particle Filtering Tractography (PFT). Structure: [ val(meta), path(pft_map_exclude) ] | |
| pft_seeding_mask | file | Seeding mask used for Particle Filtering Tractography (PFT). | |
| local_tractogram | file | Whole brain tractogram generated with Local Tracking. Structure: [ val(meta), path(local_tractogram) ] | |
| local_config | file | Configuration file used for Local Tracking. Structure: [ val(meta), path(local_config) ] | |
| local_seeding_mask | file | Seeding mask used for Local Tracking. Structure: [ val(meta), path(local_seeding_mask) ] | |
| local_tracking_mask | file | Tracking mask used for Local Tracking. Structure: [ val(meta), path(local_tracking_mask) ] | |
| versions | file | File containing software versions Structure: [ path(versions.yml) ] | versions.yml |
Components
| anatomical_segmentation |
| preproc_dwi |
| preproc_t1 |
| registration |
| reconst/dtimetrics |
| reconst/frf |
| reconst/fodf |
| reconst/meanfrf |
| registration/antsapplytransforms |
| tracking/localtracking |
| tracking/pfttracking |
Keywords
| diffusion |
| MRI |
| end-to-end |
| tractography |
| preprocessing |
| fodf |
| dti |
Authors
@AlexVCaron
Maintainers
@AlexVCaron