Page Last Updated: May 18, 2026

Diffusion MRI (dMRI)🔗

Diffusion-Weighted Imaging (DWI) measures the diffusion of water molecules in tissue and is used to model white matter microstructure and structural connectivity in the central nervous system.

Acquisition🔗

Full protocols, sequence installation, and operation instructions are available via HBCD Study MRI Protocols. In brief, the DWI protocol acquires roughly 140 diffusion-weighted echo planar images at four b-values (diffusion-weighting) between 0 and 3000 s/mm² (12-13 minutes total acquisition time). For raw image acquisition, a minimum of 60% of the diffusion-weighted volumes are required to be collected for the acquisition to be deemed successful.

Processing & Derivatives🔗

Diffusion MRI data are preprocessed using QSIPrep, which performs motion and distortion correction, MP-PCA denoising, co-registration to T1w images, spatial normalization (ANTs), and tissue segmentation (Cieslak et al. 2021, Cieslak et al. 2025). Preprocessed outputs are then passed to QSIRecon, which runs curated reconstruction workflows, including ODF/FOD reconstruction, tractography, Fixel estimation, and regional connectivity.

Pipeline	Folder	Description
QSIPrep	`qsiprep/`	Preprocessed diffusion data, transforms, QC metrics & reports
QSIRecon	`qsirecon/`	QSIRecon workflow logs and configuration files
QSIRecon-DSIStudio	`qsirecon-DSIStudio/`	DSI Studio DTI reconstruction & tractography
QSIRecon-DIPYDKI	`qsirecon-DIPYDKI/`	DIPY Diffusion kurtosis (DKI) and tensor-derived maps
QSIRecon-TORTOISE	`qsirecon-TORTOISE_model-MAPMRI/`	TORTOISE MAP-MRI and scalar maps
QSIRecon-TORTOISE	`qsirecon-TORTOISE_model-tensor/`	TORTOISE Tensor fits and scalar maps

How To Read File Trees →

hbcd/
└── derivatives/
    └── qsiprep/
        └── sub-[ID]/
            ├── log/
            └── ses-[V0X]/
                ├── anat/
                │   # Transforms
                │   ├── *_from-{ACPC_to-anat|anat_to-ACPC}_mode-image_xfm.mat
                │   ├── *_from-{ACPC_to-MNIInfant+1|MNIInfant+1_to-ACPC}_mode-image_xfm.h5
                │   ├── *_from-orig_to-anat_mode-image_xfm.txt
                │
                │   # Structural outputs (ACPC space)
                │   ├── *_space-ACPC_desc-preproc_T2w.nii.gz (+JSON)
                │   ├── *_space-ACPC_desc-{aseg_dseg|brain_mask}.nii.gz
                │   └── *_space-ACPC_dseg.nii.gz
                │
                ├── dwi/
                │   # QC & confounds
                │   ├── *_desc-confounds_timeseries.tsv
                │   ├── *_desc-{image|pepolar}_qc.tsv
                │   ├── *_space-ACPC_desc-slice_qc.json
                │
                │   # Preprocessed data
                │   ├── *_space-ACPC_desc-preproc_dwi.nii.gz (+JSON)
                │   ├── *_space-ACPC_desc-preproc_dwi.{bval|bvec|b|b_table.txt}
                │   ├── *_space-ACPC_dwiref.nii.gz
                │
                │   # Masks & maps
                │   ├── *_space-ACPC_desc-brain_mask.nii.gz
                │   └── *_space-ACPC_model-eddy_stat-cnr_dwimap.nii.gz (+JSON)
                │
                ├── figures/
                └── sub-[ID]_ses-[V0X].html

QSIRecon outputs are organized into separate derivative folders by reconstruction workflow. The qsirecon/ directory stores workflow metadata and logs. Below: Each folder corresponds to a reconstruction method; outputs are organized by session and modality (dwi/ + figures). For brevity, logs, figures, and JSON sidecars filenames are not shown.

hbcd/
└── derivatives/
    ├── qsirecon/
    │   └── sub-[ID]/
    │       └── log/*

  DSI Studio
    ├── qsirecon-DSIStudio/
    │   └── sub-[ID]/
    │       └── ses-[V0X]/
    │           ├── dwi/
    │           │   ├── *_space-ACPC_bundles-DSIStudio_{scalar|tdi}stats.tsv
    │           │   ├── *_space-ACPC_model-gqi_bundle-{BUNDLE}_streamlines.tck.gz
    │           │   ├── *_space-ACPC_model-gqi_bundlestats.csv
    │           │   ├── *_space-ACPC_model-gqi_dwimap.fib.gz
    │           │   ├── *_space-ACPC_model-gqi_dwimap.fib.gz.icbm152_adult.map.gz
    │           │   ├── *_space-ACPC_model-gqi_param-{gfa|iso|qa}_dwimap.nii.gz
    │           │   ├── *_space-{ACPC|MNIInfant+1}_model-rdi_param-{rd1|rd2}_dwimap.nii.gz
    │           │   └── *_space-{ACPC|MNIInfant+1}_model-tensor_param-{DTI-PARAM}_dwimap.nii.gz
    │           ├── figures/*
    │           └── sub-[ID]_ses-[V0X].html

  DIPY-DKI
    ├── qsirecon-DIPYDKI/
    │   └── sub-[ID]/
    │       └── ses-[V0X]/
    │           ├── dwi/
    │           │   # DIPY DKI
    │           │   ├── *_space-ACPC_bundles-DSIStudio_scalarstats.tsv
    │           │   ├── *_space-{ACPC|MNIInfant+1}_model-dki_param-{DKI-PARAM}_dwimap.nii.gz
    │           │   └── *_space-{ACPC|MNIInfant+1}_model-tensor_param-fa_dwimap.nii.gz
    │           ├── figures/*
    │           └── sub-[ID]_ses-[V0X].html

  TORTOISE MAP-MRI
    ├── qsirecon-TORTOISE_model-MAPMRI/
    │   └── sub-[ID]/
    │       └── ses-[V0X]/
    │           ├── dwi/
    │           │   ├── *_space-ACPC_bundles-DSIStudio_scalarstats.tsv
    │           │   ├── *_space-{ACPC|MNIInfant+1}_model-mapmri_param-{MAPMRI}_dwimap.nii.gz
    │           │   └── *_space-{ACPC|MNIInfant+1}_model-tensor_param-{TENSOR}_dwimap.nii.gz
    │           ├── figures/*
    │           └── sub-[ID]_ses-[V0X].html

  TORTOISE Tensor
    └── qsirecon-TORTOISE_model-tensor/
        └── sub-[ID]/
            └── ses-[V0X]/
                └── dwi/
                    ├── *_space-ACPC_bundles-DSIStudio_scalarstats.tsv
                    └── *_space-MNIInfant+1_model-tensor_param-{TENSOR}_dwimap.nii.gz

# Label Values Legend
Prefix: sub-[ID]_ses-[V0X]
DTI-PARAM: ad, fa, ha, md, rd, txx, txy, txz, tyy, tyz, tzz
DKI-PARAM: ad, ak, kfa, md, mk, mkt, rd, rk
MAPMRI: ng, ngpar, ngperp, pa, path, rtap, rtop, rtpp
TENSOR: ad, am, fa, li, rd

# DSI Studio {BUNDLE} groups: → See full DSIStudio bundle label list
Association{LABEL}, Cerebellum{LABEL}, Commissure{LABEL}, ProjectionBasalGanglia{LABEL}, ProjectionBrainstem{LABEL}

{BUNDLE} Values	Nested Bundle {LABEL} Values
Association{LABEL}	Cingulum{L/R}ExtremeCapsule{L/R}FrontalAslantTract{L/R}ParietalAslantTract{L/R}HippocampusAlveus{L/R}ArcuateFasciculus{L/R}AssociationUncinateFasciculus{L/R}InferiorFrontoOccipitalFasciculus{L/R}InferiorLongitudinalFasciculus{L/R}MiddleLongitudinalFasciculus{L/R}SuperiorLongitudinalFasciculus{L/R}VerticalOccipitalFasciculus{L/R}
Cerebellum{LABEL}	Cerebellum{L/R}InferiorCerebellarPeduncle{L/R}MiddleCerebellarPeduncleSuperiorCerebellarPeduncleVermis
Commissure{LABEL}	AnteriorCommissureCorpusCallosum
ProjectionBasalGanglia{LABEL}	AcousticRadiation{L/R}OpticRadiation{L/R}ThalamicRadiation{L/R}AnsaLenticularis{L/R}FasciculusLenticularis{L/R}AnsaSubthalamic{L/R}FasciculusSubthalamicus{L/R}CorticostriatalTract{L/R}Fornix{L/R}
ProjectionBrainstem{LABEL}	CorticobulbarTract{L/R}CorticopontineTract{L/R}CorticospinalTract{L/R}ReticularTract{L/R}DentatorubrothalamicTract{lr/rl}NonDecussatingDentatorubrothalamicTract{L/R}MedialForebrainBundle{L/R}MedialLemniscus{L/R}

dMRI Derivatives Quick Start Guide🔗

The diffusion encoding provided via the multiple QSIRecon derivative folders enable the estimation of multiple diffusion MRI models to create the derived data, including:

Diffusion Tensor Imaging (DTI)🔗

DSI Studio models diffusion with a 3D Gaussian distribution of water displacements. Key outputs include fractional anisotropy (FA), i.e. anisotropic diffusion (typically higher in white matter bundles with dense, parallel fibers) and mean diffusivity (MD), i.e. directionally averaged apparent diffusion coefficient (inversely related to cellular membrane density) (Basser 1994).
Derivatives: qsirecon-DSIStudio/

Diffusion Kurtosis Imaging (DKI)🔗

DKI extends DTI to capture non-Gaussian diffusion. The main metric is mean kurtosis (MK), which is more sensitive to complex or restricted diffusion and often higher in dense white matter (Jensen 2005).
Derivatives: qsirecon-DIPYDKI/

Mean Apparent Propagator MRI (MAP-MRI)🔗

MAP-MRI Extends DTI by estimating the full spatial probability distribution (propagator) of water diffusion without assuming Gaussian distribution. This enables quantification of non-Gaussian diffusion and more accurate measures of directionality and anisotropy (Özarslan 2013).
Derivatives: qsirecon-TORTOISE_model-MAPMRI/

Metric	Description
Propagator Anisotropy (PA)	Quantifies anisotropy by computing the dissimilarity of the full MAP-MRI propagator from its fully isotropic counterpart. More accurate than FA.
Non-Gaussianity (NG)	Quantifies deviation from Gaussian diffusion. NG measures overall deviation, NGpar along the primary diffusion axis (fiber direction in white matter), and NGperp perpendicular to it (often related to restriction).
Return To Origin Probability (RTOP)	Probability that a water molecule returns to its starting point. Low in unrestricted diffusion (large cells), high in restricted diffusion (small or impermeable cells). Inversely related to pore volume.
Return To Axis Probability (RTAP)	Probability that a water molecule returns to the principal diffusion axis (primary eigenvector).
Return To Plane Probability (RTPP)	Reciprocal of mean cylinder length and inversely proportional to axial diffusivity; Related to diffusion taking place within coherently oriented cylinders.

QSIRecon Workflow	Model (Shells)	Parameters	Description
DSI Studio	gqi (Full shells)	gfa	Generalized fractional anisotropy
		iso	Isotropic diffusion component
		qa	Quantitative anisotropy
	tensor (Inner shells)	fa	Fractional anisotropy
		ad / md / rd	Axial / Mean / Radial diffusivity
		rd1 / rd2	Second and third eigenvalues (λ₂ / λ₃)
		ha	Helix angle
		txx / txy / txz / tyy / tyz / tzz	Diffusion tensor elements
DIPY DKI	dki (Full shells)	ad / ak	Axial diffusivity / Axial kurtosis
		fa / kfa	Fractional anisotropy / Kurtosis FA
		md / mk / mkt	Mean diffusivity / Mean kurtosis / Mean kurtosis tensor
		rd / rk	Radial diffusivity / Radial kurtosis
TORTOISE- MAPMRI	mapmri (Full shells)	ng / ngpar / ngperp	Non-Gaussianity / Parallel NG / Perpendicular NG
		fa / kfa	Fractional anisotropy / Kurtosis FA
		pa / path	Propagator anisotropy / Thresholded PA
		rtap / rtop / rtpp	Return-to-axis / origin / plane probability
	tensor (Inner shells)	ad / rd	Axial / Radial diffusivity
		am / fa	A0 (mean signal) / Fractional anisotropy
		li	Lattice index
TORTOISE- Tensor	tensor (Full shells)	ad / rd	Axial / Radial diffusivity
		am / fa	A0 (mean signal) / Fractional anisotropy
		li	Lattice index

Quality Control Summary Statistics🔗

Automated QC for processed diffusion data is fairly robust, with metrics provided in sub-[ID]_ses-[V0X]_space-ACPC_desc-image_qc.tsv within the QSIPrep derivatives (see QSIPrep documentation for details). Below are distributions of automated QC metrics from HBCD visits V02 and V03. Higher Neighboring DWI Correlation (NDC; closer to 1) and Contrast-to-Noise Ratio (CNR) indicate better image quality. NDC can also be used as a covariate in analyses to account for QC variation.

Left: NDC calculated pre- and post-processing for each vendor using combined AP/PA scans
Right: Shell-wise CNR calculated by Eddy. We do not provide exclusion threshold recommendations because all data passed preliminary QC. However, NDC and CNR are useful covariates when analyzing other derivatives.

References🔗

Alexander AL, Lee JE, Lazar M, Field AS. (2007). Diffusion tensor imaging of the brain. Neurotherapeutics, 4(3):316-29. 10.1016/j.nurt.2007.05.011

Basser PJ, Mattiello J, LeBihan D. (1994). MR diffusion tensor spectroscopy and imaging. Biophys J., 66(1):259-67. 10.1016/S0006-3495(94)80775-1

Cieslak M, Cook PA, He X, Yeh FC, Dhollander T, Adebimpe A, Aguirre GK, Bassett DS, Betzel RF, Bourque J, Cabral LM, Davatzikos C, Detre JA, Earl E, Elliott MA, Fadnavis S, Fair DA, Foran W, Fotiadis P, Garyfallidis E, Giesbrecht B, Gur RC, Gur RE, Kelz MB, Keshavan A, Larsen BS, Luna B, Mackey AP, Milham MP, Oathes DJ, Perrone A, Pines AR, Roalf DR, Richie-Halford A, Rokem A, Sydnor VJ, Tapera TM, Tooley UA, Vettel JM, Yeatman JD, Grafton ST, Satterthwaite TD. (2021). QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nature Methods, 18(7):775-778. 10.1038/s41592-021-01185-5

Cieslak, M., Irfanoglu, M. O., Meisler, S. L., Salo, T., Raikes, A. C., Cook, P. A., Chung, A. W., Lee, E. G., Li, R., Li, X., Pecheva, D., Fair, D. A., Smyser, C. D., Harms, M. P., Landman, B. A., Wisnowski, J. L., Huang, H., Alexander, A. L., & Satterthwaite, T. D. (2025). Diffusion MRI processing in the HEALthy Brain and child development study: Innovations and applications. In bioRxiv. https://doi.org/10.1101/2025.11.10.687672

Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K. (2005). Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic Resonance in Medicine, 53(6), 1432–1440. https://doi.org/10.1002/mrm.20508

Özarslan E, Koay CG, Shepherd TM, Komlosh ME, İrfanoğlu MO, Pierpaoli C, Basser PJ. (2013). Mean apparent propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage, 78:16-32. 10.1016/j.neuroimage.2013.04.016