Built-In Reconstruction Workflows

The Built-In recon workflows can be easily selected by specifying their name after the --recon-spec flag (e.g. --recon-spec amico_noddi). Many of these workflows were originally described in Cieslak et al.[1]. Not all workflows are suitable for all kinds of dMRI data. Be sure to check Which workflows are appropriate for your dMRI data?.

By specifying just a name for --recon_spec, you will be using all the default arguments for the various steps in that workflow. Workflows can be customized (see Custom Reconstruction Workflows).

Workflows

MRtrix3-based Workflows

The MRtrix workflows are identical up to the FOD estimation. In each case the fiber response function is estimated using dwi2response dhollander [2] with a mask based on the T1w. The main differences are in

• the CSD algorithm used in dwi2fod (msmt_csd or ss3t_csd)

• whether a T1w-based tissue segmentation is used during tractography

In the *_noACT versions of the pipelines, no T1w-based segmentation is used during tractography. Otherwise, cropping is performed at the GM/WM interface, along with backtracking.

In all pipelines, tractography is performed using tckgen, which uses the iFOD2 probabilistic tracking method to generate 1e7 streamlines with a maximum length of 250mm, minimum length of 30mm, FOD power of 0.33. Weights for each streamline were calculated using SIFT2 [3] and were included for while estimating the structural connectivity matrix.

Warning

We don’t recommend using ACT with FAST segmentations. The full benefits of ACT require very precise tissue boundaries and FAST just doesn’t do this reliably enough. We strongly recommend the hsvs segmentation if you’re going to use ACT. Note that this requires --freesurfer-input

MRtrix3 DWI Outputs

These files are located in the dwi/ directories.

File Name	Description
*_connectivity.mat	MATLAB format mat file containing connectivity matrices for all the selected atlases. This is an hdf5-format file and can be read using scipy.io.matlab.loadmat in Python.
*_exemplarbundles.zip	A zip archive containing the output directory from connectome2tck. Unzip this directory and view the exemplar connections using mrview to ensure that you’re seeing the expected shapes of connections.
*_streamlines.tck.gz	Streamlines produced by tckgen. NOTE: these are not saved to the output directory by default.
*model-mtnorm*param-inliermask*_dwimap.nii.gz	Inlier mask created by mtnormalize
*model-mtnorm*param-norm*_dwimap.nii.gz	Inlier mask created by mtnormalize
*model-sift2*_mu.txt	The $mu$ value that should be used to adjust SIFT2 weights to account for different response functions.
*model-sift2*_streamlineweights.csv	Per-streamline SIFT2 weight for each streamline in streamlines.tck.gz.
*param-fod*label-CSF*_dwimap.mif.gz	FOD for cerebrospinal fluid.
*param-fod*label-CSF*_dwimap.txt	SH response function for cerebrospinal fluid.
*param-fod*label-GM*_dwimap.mif.gz	FOD for gray matter.
*param-fod*label-GM*_dwimap.txt	SH response function for gray matter.
*param-fod*label-WM*_dwimap.mif.gz	FOD for white matter. These FODs are used as inputs to tckgen for tractograpy.
*param-fod*label-WM*_dwimap.txt	SH response function for white matter.

MRtrix3 Anatomical Outputs

These files are located anat/ directories.

File Name	Description
*space-T1w*atlas-hsvs*_dseg.nii.gz	Hybrid Surface/Voume Segmentation in MRtrix3 5tt format. Aligned in coordinate space to space-T1w.
*space-fsnative*atlas-hsvs*_dseg.nii.gz	Hybrid Surface/Voume Segmentation in MRtrix3 5tt format. Aligned to the FreeSurfer orig.mgz image.

mrtrix_multishell_msmt_ACT-hsvs

This workflow uses the msmt_csd algorithm [4] to estimate FODs for white matter, gray matter and cerebrospinal fluid using multi-shell acquisitions. The white matter FODs are used for tractography and the T1w segmentation is used for anatomical constraints [5]. The T1w segmentation uses the hybrid surface volume segmentation (hsvs) [6] and requires --freesurfer-input. This workflow produces MRtrix3 DWI Outputs and MRtrix3 Anatomical Outputs.

mrtrix_multishell_msmt_ACT-fast

Identical to mrtrix_multishell_msmt_ACT-hsvs except FSL’s FAST is used for tissue segmentation. This workflow is not recommended. This workflow produces MRtrix3 DWI Outputs.

mrtrix_multishell_msmt_noACT

This workflow uses the msmt_csd algorithm [4] to estimate FODs for white matter, gray matter and cerebrospinal fluid using multi-shell acquisitions. The white matter FODs are used for tractography with no T1w-based anatomical constraints. This workflow produces MRtrix3 DWI Outputs.

mrtrix_singleshell_ss3t_ACT-hsvs

This workflow uses the ss3t_csd_beta1 algorithm [7] to estimate FODs for white matter, and cerebrospinal fluid using single shell (DTI) acquisitions. The white matter FODs are used for tractography and the T1w segmentation is used for anatomical constraints [5]. The T1w segmentation uses the hybrid surface volume segmentation (hsvs) [6] and requires --freesurfer-input. This workflow produces MRtrix3 DWI Outputs and MRtrix3 Anatomical Outputs.

mrtrix_multishell_msmt_ACT-fast

Identical to mrtrix_singleshell_ss3t_ACT-hsvs except FSL’s FAST is used for tissue segmentation. This workflow is not recommended. This workflow produces MRtrix3 DWI Outputs.

mrtrix_singleshell_ss3t_noACT

This workflow uses the ss3t_csd_beta1 algorithm [7] to estimate FODs for white matter, and cerebrospinal fluid using single shell (DTI) acquisitions. The white matter FODs are used for tractography with no T1w-based anatomical constraints. This workflow produces MRtrix3 DWI Outputs.

pyafq_tractometry

This workflow uses the AFQ [8] implemented in Python [9] to recognize major white matter pathways within the tractography, and then extract tissue properties along those pathways. See the pyAFQ documentation .

PyAFQ Outputs

File Name	Description
sub-* (directory)	PyAFQ results direcrory for each subject

mrtrix_multishell_msmt_pyafq_tractometry

Identical to pyafq_tractometry except that tractography generated using IFOD2 from MRTrix3, instead of using pyAFQ’s default DIPY tractography. This can also be used as an example for how to import tractographies from other reconstruciton pipelines to pyAFQ. This workflow produces MRtrix3 DWI Outputs.

PyAFQ Outputs

File Name	Description
sub-* (directory)	PyAFQ results direcrory for each subject

amico_noddi

This workflow estimates the NODDI [10] model using the implementation from AMICO [11]. Images with intra-cellular volume fraction (ICVF), isotropic volume fraction (ISOVF), orientation dispersion (OD) are written to outputs. Additionally, a DSI Studio fib file is created using the peak directions and ICVF as a stand-in for QA to be used for tractography.

Scalar Maps

Model	Parameter	Description
noddi	direction	Peak directions from NODDI
noddi	icvf	Intracellular volume fraction from NODDI
noddi	isovf	Isotropic volume fraction from NODDI
noddi	od	Orientation dispersion index from NODDI

Other Outputs

File Name	Description
*model-noddi*_config.pickle.gz	A config file internally used by AMICO.
*model-noddi*param-direction*_dwimap.fib.gz	DSI Studio fib format file where the peak directions come from the NODDI fit. The “qa” variable is actually ICVF.

dsi_studio_gqi

Here the standard GQI plus deterministic tractography pipeline is used [12]. GQI works on almost any imaginable sampling scheme because DSI Studio will internally interpolate the q-space data so symmetry requirements are met. GQI models the water diffusion ODF, so ODF peaks are much smaller than you see with CSD. This results in a rather conservative peak detection, which greatly benefits from having more diffusion data than a typical DTI.

5 million streamlines are created with a maximum length of 250mm, minimum length of 30mm, random seeding, a step size of 1mm and an automatically calculated QA threshold.

Additionally, a number of anisotropy scalar images are produced such as QA, GFA and ISO.

Scalar Maps

Model	Parameter	Description
gqi	gfa	Generalized Fractional Anisotropy
gqi	iso	Isotropic Diffusion
gqi	qa	Fractional Anisotropy from a tensor fit
rdi	rd1	RD1
rdi	rd2	RD2
tensor	ad	AD
tensor	fa	Radial Diffusivity from a tensor fit
tensor	ha	HA
tensor	md	Mean Diffusivity
tensor	rd	Radial Diffusivity
tensor	txx	Tensor fit txx
tensor	txy	Tensor fit txy
tensor	txz	Tensor fit txz
tensor	tyy	Tensor fit tyy
tensor	tyz	Tensor fit tyz
tensor	tzz	Tensor fit tzz

Other Outputs

File Name	Description
*_connectivity.mat	MATLAB format mat file containing connectivity matrices for all the selected atlases. This is an hdf5-format file and can be read using scipy.io.matlab.loadmat in Python.
*space-T1w*_dwimap.fib.gz	DSI Studio fib format containing the GQI ODFs used for AutoTrack.
*space-T1w*_mapping.map.gz	Mapping file produced by DSI Studio.

dsi_studio_autotrack

This workflow implements DSI Studio’s q-space diffeomorphic reconstruction (QSDR), the MNI space (ICBM-152) version of GQI, followed by automatic fiber tracking (autotrack) [13][14] of 56 white matter pathways. Autotrack uses a population-averaged tractography atlas (based on HCP-Young Adult data) to identify tracts of interest in individual subject’s data. The autotrack procedure seeds deterministic fiber tracking with randomized parameter saturation within voxels that correspondto each tract in the tractography atlas and determines whether generated streamlines belong to the target tract based on the Hausdorff distance between subject and atlas streamlines.

Reconstructed subject-specific tracts are written out as .tck files that are aligned to the qsirecon-generated _dwiref.nii.gz and preproc_T1w.nii.gz volumes; .tck files can be visualized overlaid on these volumes in mrview or MI-brain. Note, .tck files will not appear in alignment with the dwiref/T1w volumes in DSI Studio due to how the .tck files are read in.

Diffusion metrics (e.g., dti_fa, gfa, iso,rdi, nrdi02) and shape statistics (e.g., mean_length, span, curl, volume, endpoint_radius) are calculated for subject-specific tracts and written out in an AutoTrackGQI.csv file.

Scalar Maps

Model	Parameter	Description
gqi	gfa	Generalized Fractional Anisotropy
gqi	iso	Isotropic Diffusion
gqi	qa	Fractional Anisotropy from a tensor fit
rdi	rd1	RD1
rdi	rd2	RD2
tensor	ad	AD
tensor	fa	Radial Diffusivity from a tensor fit
tensor	ha	HA
tensor	md	Mean Diffusivity
tensor	rd	Radial Diffusivity
tensor	txx	Tensor fit txx
tensor	txy	Tensor fit txy
tensor	txz	Tensor fit txz
tensor	tyy	Tensor fit tyy
tensor	tyz	Tensor fit tyz
tensor	tzz	Tensor fit tzz

Other Outputs

File Name	Description
*_streamlines.tck.gz	One tck.gz per bundle. The bundle represented by this file is specified in the bundle- tag.
*bundles-DSIStudio*_scalarstats.csv	Statistics on scalars produced by this workflow
*bundles-DSIStudio*_tdistats.tsv	Statistics on streamline density in voxels
*space-T1w*_dwimap.fib.gz	DSI Studio fib format containing the GQI ODFs used for AutoTrack.
*space-T1w*_mapping.map.gz	Mapping file produced by DSI Studio.

ss3t_autotrack

This workflow is identical to dsi_studio_autotrack, except it substitutes the GQI fit with the ss3t_csd_beta1 algorithm [7] to estimate FODs for white matter.

This is a good workflow for doing tractometry on low-quality single shell data.

Scalar Maps

Model	Parameter	Description
gqi	gfa	Generalized Fractional Anisotropy
gqi	iso	Isotropic Diffusion
gqi	qa	Fractional Anisotropy from a tensor fit
rdi	rd1	RD1
rdi	rd2	RD2
tensor	ad	AD
tensor	fa	Radial Diffusivity from a tensor fit
tensor	ha	HA
tensor	md	Mean Diffusivity
tensor	rd	Radial Diffusivity
tensor	txx	Tensor fit txx
tensor	txy	Tensor fit txy
tensor	txz	Tensor fit txz
tensor	tyy	Tensor fit tyy
tensor	tyz	Tensor fit tyz
tensor	tzz	Tensor fit tzz

Other Outputs

File Name	Description
*_streamlines.tck.gz	One tck.gz per bundle. The bundle represented by this file is specified in the bundle- tag.
*bundles-DSIStudio*_scalarstats.csv	Statistics on scalars produced by this workflow
*bundles-DSIStudio*_tdistats.tsv	Statistics on streamline density in voxels
*space-T1w*_dwimap.fib.gz	DSI Studio fib format containing the SS3T FODs used for AutoTrack.
*space-T1w*_mapping.map.gz	Mapping file produced by DSI Studio.

TORTOISE

The TORTOISE [15] software can calculate Tensor and MAPMRI fits, along with their many associated scalar maps. This workflow only produces scalar maps.

Scalar Maps

Model	Parameter	Description
mapmri	ng	Non-Gaussianity from MAPMRI
mapmri	ngpar	Non-Gaussianity parallel from MAPMRI
mapmri	ngperp	Non-Gaussianity perpendicular from MAPMRI
mapmri	pa	PA from MAPMRI
mapmri	path	PAth from MAPMRI
mapmri	rtap	Return to axis probability from MAPMRI
mapmri	rtop	Return to origin probability from MAPMRI
mapmri	rtpp	Return to plane probability from MAPMRI
tensor	ad	Apparent Diffusivity from a tensor fit
tensor	am	A0 from a tensor fit
tensor	fa	Fractional Anisotropy from a tensor fit
tensor	li	LI from a tensor fit
tensor	rd	Radial Diffusivity from a tensor fit

Other Outputs

File Name	Description
*_scalarstats.tsv	TORTOISE scalars (tensors and MAPMRI) summarized within WM bundles. The name of the method used to create the bundles is specified after bundles-.

dipy_mapmri

The MAPMRI method is used to estimate EAPs from which ODFs are calculated analytically. This method produces scalars like RTOP, RTAP, QIV, MSD, etc.

The ODFs are saved in DSI Studio format and tractography is run identically to that in dsi_studio_gqi.

Scalar Maps

Model	Parameter	Description
mapmri	lapnorm	Laplacian norm from regularized MAPMRI (MAPL)
mapmri	mapcoeffs	MAPMRI coefficients
mapmri	msd	mean square displacement from MAPMRI
mapmri	ng	Non-Gaussianity from MAPMRI
mapmri	ngpar	Non-Gaussianity parallel from MAPMRI
mapmri	ngperp	Non-Gaussianity perpendicular from MAPMRI
mapmri	qiv	q-space inverse variance from MAPMRI
mapmri	rtap	Return to axis probability from MAPMRI
mapmri	rtop	Return to origin probability from MAPMRI
mapmri	rtpp	Return to plane probability from MAPMRI

Other Outputs

File Name	Description
*_scalarstats.tsv	MAPMRI scalars summarized within WM bundles. The name of the method used to create the bundles is specified after bundles-.

dipy_dki

A DKI model is fit to the dMRI signal and multiple scalar maps are produced.

Scalar Maps

Model	Parameter	Description
dki	ad	DKI AD
dki	ak	DKI AK
dki	kfa	DKI KFA
dki	md	DKI MD
dki	mk	DKI MK
dki	mkt	DKI MKT
dki	rd	DKI RD
dki	rk	DKI RK
tensor	fa	DKI FA

Other Outputs

File Name	Description
*_scalarstats.tsv	DKI scalars summarized within WM bundles. The name of the method used to create the bundles is specified after bundles-.

dipy_3dshore

This uses the BrainSuite 3dSHORE basis in a Dipy reconstruction. Much like dipy_mapmri, a slew of anisotropy scalars are estimated. Here the dsi_studio_gqi fiber tracking is again run on the 3dSHORE-estimated ODFs.

Scalar Maps

Model	Parameter	Description
3dshore	CNR	Contrast to noise ratio for 3dshore fit
3dshore	alpha	alpha used when fitting in each voxel
3dshore	lapnorm	Laplacian norm from regularized MAPMRI (MAPL)
3dshore	mapcoeffs	MAPMRI coefficients
3dshore	msd	mean square displacement from MAPMRI
3dshore	ng	Non-Gaussianity from MAPMRI
3dshore	ngpar	Non-Gaussianity parallel from MAPMRI
3dshore	ngperp	Non-Gaussianity perpendicular from MAPMRI
3dshore	qiv	q-space inverse variance from MAPMRI
3dshore	r2	r^2 of the 3dshore fit
3dshore	regularization	regularization of the 3dshore fit
3dshore	rtap	Return to axis probability from MAPMRI
3dshore	rtop	Return to origin probability from MAPMRI
3dshore	rtpp	Return to plane probability from MAPMRI

reorient_fslstd

Reorients the qsirecon preprocessed DWI and bval/bvec to the standard FSL orientation. This can be useful if FSL tools will be applied outside of qsirecon.

csdsi_3dshore

[EXPERIMENTAL] This pipeline is for DSI or compressed-sensing DSI. The first step is a L2-regularized 3dSHORE reconstruction of the ensemble average propagator in each voxel. These EAPs are then used for two purposes

1. To calculate ODFs, which are then sent to DSI Studio for tractography

2. To estimate signal for a multishell (specifically HCP) sampling scheme, which is run through the pipeline

All outputs, including the imputed HCP sequence are saved in the outputs directory.

Scalar Maps

Model	Parameter	Description
3dshore	CNR	Contrast to noise ratio for 3dshore fit
3dshore	alpha	alpha used when fitting in each voxel
3dshore	lapnorm	Laplacian norm from regularized MAPMRI (MAPL)
3dshore	mapcoeffs	MAPMRI coefficients
3dshore	msd	mean square displacement from MAPMRI
3dshore	ng	Non-Gaussianity from MAPMRI
3dshore	ngpar	Non-Gaussianity parallel from MAPMRI
3dshore	ngperp	Non-Gaussianity perpendicular from MAPMRI
3dshore	qiv	q-space inverse variance from MAPMRI
3dshore	r2	r^2 of the 3dshore fit
3dshore	regularization	regularization of the 3dshore fit
3dshore	rtap	Return to axis probability from MAPMRI
3dshore	rtop	Return to origin probability from MAPMRI
3dshore	rtpp	Return to plane probability from MAPMRI

Other Outputs

File Name	Description
*_scalarstats.tsv	MAPMRI scalars summarized within WM bundles. The name of the method used to create the bundles is specified after bundles-.

hbcd_scalar_maps

Designed to run on [HBCD](https://hbcdstudy.org/) data, this is also a general-purpose way to get many multishell-supported fitting methods, including

• dipy_dki

• TORTOISE (model-MAPMRI)

• TORTOISE (model-tensor using only b<4000)

• amico_noddi

• dsi_studio_gqi

Bundles are generated using dsi_studio_autotrack. All the scalars generated by these models are then mapped

1. Into template space

2. On to the bundles from dsi_studio_autotrack

In total, the scalars estimated by this workflow are:

Scalar Maps

Model	Parameter	Description
dki	ad	DKI AD
dki	ak	DKI AK
dki	kfa	DKI KFA
dki	md	DKI MD
dki	mk	DKI MK
dki	mkt	DKI MKT
dki	rd	DKI RD
dki	rk	DKI RK
gqi	gfa	Generalized Fractional Anisotropy
gqi	iso	Isotropic Diffusion
gqi	qa	Fractional Anisotropy from a tensor fit
mapmri	ng	Non-Gaussianity from MAPMRI
mapmri	ngpar	Non-Gaussianity parallel from MAPMRI
mapmri	ngperp	Non-Gaussianity perpendicular from MAPMRI
mapmri	pa	PA from MAPMRI
mapmri	path	PAth from MAPMRI
mapmri	rtap	Return to axis probability from MAPMRI
mapmri	rtop	Return to origin probability from MAPMRI
mapmri	rtpp	Return to plane probability from MAPMRI
noddi	direction	Peak directions from NODDI
noddi	icvf	Intracellular volume fraction from NODDI
noddi	isovf	Isotropic volume fraction from NODDI
noddi	od	Orientation dispersion index from NODDI
rdi	rd1	RD1
rdi	rd2	RD2
tensor	ad	AD
tensor	am	A0 from a tensor fit
tensor	fa	DKI FA
tensor	fa	Fractional Anisotropy from a tensor fit
tensor	fa	Radial Diffusivity from a tensor fit
tensor	ha	HA
tensor	li	LI from a tensor fit
tensor	md	Mean Diffusivity
tensor	rd	Radial Diffusivity
tensor	txx	Tensor fit txx
tensor	txy	Tensor fit txy
tensor	txz	Tensor fit txz
tensor	tyy	Tensor fit tyy
tensor	tyz	Tensor fit tyz
tensor	tzz	Tensor fit tzz

Other Outputs

File Name	Description
*_connectivity.mat	MATLAB format mat file containing connectivity matrices for all the selected atlases. This is an hdf5-format file and can be read using scipy.io.matlab.loadmat in Python.
*space-T1w*_dwimap.fib.gz	DSI Studio fib format containing the GQI ODFs used for AutoTrack.
*space-T1w*_mapping.map.gz	Mapping file produced by DSI Studio.

Which workflows are appropriate for your dMRI data?

Most reconstruction workflows will fit a model to the dMRI data. Listed below are the model-fitting workflows and which sampling schemes work with them.

Name	MultiShell	Cartesian	SingleShell
mrtrix_multishell_msmt_ACT-fast*	Yes	No	No
mrtrix_multishell_msmt_ACT-hsvs	Yes	No	No
mrtrix_multishell_msmt_noACT	Yes	No	No
mrtrix_singleshell_ss3t_noACT	No	No	Yes
mrtrix_singleshell_ss3t_ACT-hsvs	No	No	Yes
mrtrix_multishell_msmt_ACT-fast*	No	No	Yes
pyafq_tractometry	Yes	No	Yes
mrtrix_multishell_msmt_pyafq_tractometry	Yes	No	Yes
amico_noddi	Yes	No	No
TORTOISE	Yes	No	No
dsi_studio_gqi	Yes	Yes	Yes*
dsi_studio_autotrack	Yes	Yes	Yes
ss3t_autotrack	No	No	Yes
dipy_mapmri	Yes	Yes	No
dipy_dki	Yes	No	No
dipy_3dshore	Yes	Yes	No
csdsi_3dshore	Yes	Yes	No
hbcd_scalar_maps	Yes	No	No

* Not recommended

References

[1]

Matthew Cieslak, Philip A Cook, Xiaosong He, Fang-Cheng Yeh, Thijs Dhollander, Azeez Adebimpe, Geoffrey K Aguirre, Danielle S Bassett, Richard F Betzel, Josiane Bourque, and others. Qsiprep: an integrative platform for preprocessing and reconstructing diffusion mri data. Nature methods, 18(7):775–778, 2021. URL: https://doi.org/10.1038/s41592-021-01185-5, doi:10.1038/s41592-021-01185-5.

[2]

T Dhollander, R Mito, D Raffelt, and A Connelly. Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. In Proc. Intl. Soc. Mag. Reson. Med, 555. 2019.

[3]

Robert E Smith, Jacques-Donald Tournier, Fernando Calamante, and Alan Connelly. Sift2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage, 119:338–351, 2015. URL: https://doi.org/10.1016/j.neuroimage.2015.06.092, doi:10.1016/j.neuroimage.2015.06.092.

[4] (1,2)

Ben Jeurissen, Jacques-Donald Tournier, Thijs Dhollander, Alan Connelly, and Jan Sijbers. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion mri data. NeuroImage, 103:411–426, 2014.

[5] (1,2)

Robert E Smith, Jacques-Donald Tournier, Fernando Calamante, and Alan Connelly. Anatomically-constrained tractography: improved diffusion mri streamlines tractography through effective use of anatomical information. Neuroimage, 62(3):1924–1938, 2012. URL: https://doi.org/10.1016/j.neuroimage.2012.06.005, doi:10.1016/j.neuroimage.2012.06.005.

[6] (1,2)

Robert Smith, Antonin Skoch, Claude J Bajada, Svenja Caspers, and Alan Connelly. Hybrid surface-volume segmentation for improved anatomically-constrained tractography. OHBM, 2020. URL: https://www.um.edu.mt/library/oar/handle/123456789/59839.

[7] (1,2,3)

Thijs Dhollander and Alan Connelly. A novel iterative approach to reap the benefits of multi-tissue csd from just single-shell (+ b= 0) diffusion mri data. In Proc ISMRM, volume 24, 3010. 2016.

[8]

Jason D. Yeatman, Robert F. Dougherty, Nathaniel J. Myall, Brian A. Wandell, and Heidi M. Feldman. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS ONE, 2012. doi:10.1371/journal.pone.0049790.

[9]

J. Kruper, J.D. Yeatman, A. Richie-Halford, D. Bloom, M. Grotheer, S. Caffarra, G. Kiar, I.I. Karipidis, E. Roy, B.Q. Chandio, E. Garyfallidis, and A. Rokem. Evaluating the reliability of human brain white matter tractometry. Aperture Neuro, 1:1–25, 2021.

[10]

Hui Zhang, Torben Schneider, Claudia A Wheeler-Kingshott, and Daniel C Alexander. Noddi: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4):1000–1016, 2012.

[11]

Alessandro Daducci, Erick J Canales-Rodríguez, Hui Zhang, Tim B Dyrby, Daniel C Alexander, and Jean-Philippe Thiran. Accelerated microstructure imaging via convex optimization (amico) from diffusion mri data. NeuroImage, 105:32–44, 2015.

[12]

Fang-Cheng Yeh, Timothy D Verstynen, Yibao Wang, Juan C Fernández-Miranda, and Wen-Yih Isaac Tseng. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PloS one, 8(11):e80713, 2013.

[13]

Fang-Cheng Yeh. Shape analysis of the human association pathways. Neuroimage, 223:117329, 2020.

[14]

Fang-Cheng Yeh. Population-based tract-to-region connectome of the human brain and its hierarchical topology. Nature communications, 13(1):4933, 2022. URL: https://doi.org/10.1038/s41467-022-32595-4, doi:10.1038/s41467-022-32595-4.

[15]

Mustafa Okan Irfanoglu, Amritha Nayak, Jeffrey Jenkins, and Carlo Pierpaoli. Tortoise v3: improvements and new features of the nih diffusion mri processing pipeline. In Program and proceedings of the ISMRM 25th annual meeting and exhibition, Honolulu, HI, USA. 2017.