neuroreg 0.4.0.dev0

A package for 3D neuroimaging registration with PyTorch.

NeuroReg

NeuroReg is a package for the robust registration of 3D neuroimaging data (e.g. MRI). It supports both image-to-image (within and cross modal) as well as image to landmark (WM-surface) or segmentation-based registration, with a focus on high accuracy and speed.

The main user-facing tools are:

• robreg – Highly accurate and robust same modality image-to-image registration (IRLS based, analogous to FreeSurfer's mri_robust_register)
• coreg – Image-to-image cross-modal registration (Powell-based by default, analogous to FreeSurfer/SPM mri_coreg)
• bbreg – boundary-based registration using cortical WM surfaces or segmentations (extending FreeSurfer's bbregister)
• segreg – segmentation-based registration via label centroids (rigid/affine, including atlas-centroid and upright/self-flip modes)
• lta – transform comparison, inversion, concatenation, and conversion utilities

This project is a work-in-progress in an early development stage. It is developed by the creator of FreeSurfer's mri_robust_register as an efficient pure Python replacement (with GPU support) and cross-modal extensions to support all your medical imaging registration needs. If you find it useful for a publication, please cite the relevant papers (see References).

Installation

pip install neuroreg

Command-line interface

robreg — image-to-image registration

Registers a moving image to a reference image using a multi-resolution IRLS-based robust registration path with Tukey weighting.

This path is currently intended for same-contrast or very similar-contrast image pairs. On Apple MPS, the current public IRLS path falls back to CPU with a warning due to lack of double precision support on MPS.

robreg --mov <moving.nii.gz> --ref <reference.nii.gz> --out <output.lta> [options]

Run robreg -h for a full argument summary with defaults.

Required arguments

Argument	Description
--mov FILE	Moving (source) image (NIfTI or MGZ).
--ref FILE	Reference (target/fixed) image (NIfTI or MGZ).
--out LTA	Output LTA file for the recovered transformation.

Options

Argument	Default	Description
--dof {6}	6	Degrees of freedom. The public robreg path is currently rigid-only.
--nmax N	5	Maximum number of outer IRLS iterations per pyramid level.
--sat FLOAT	6.0	Tukey biweight saturation threshold.
--nosym	off	Disable symmetric halfway-space registration and run directed registration. Symmetric registration is the default.
--init-header	off	Use header alignment only.
--init-centroid	default	Initialize by aligning intensity centroids in RAS.
--init-center	off	Initialize by aligning geometric image centers in RAS.
--mapped FILE	—	Save the warped moving image.
--outliers FILE	—	Save an outlier map (1 - Tukey weights).
--verbose	off	Enable INFO-level logging.
--debug	off	Enable DEBUG-level logging.

Example

robreg --mov T1_repeat.nii.gz --ref T1_baseline.mgz --out T1_repeat_to_T1_baseline.lta --verbose

---

coreg — image-based cross-modal registration

This is the package's image-to-image registration path for cross-sequence / cross-modal alignment when no surfaces or segmentation are available. By default it runs a Powell-based MRI_coreg-style pipeline (coarse brute-force search plus Powell refinement), similar in spirit to FreeSurfer/SPM mri_coreg; the older PyTorch gradient-descent path remains available via --method gd.

coreg --mov <moving.nii.gz> --ref <reference.nii.gz> --out <output.lta> [options]

Options

Argument	Default	Description
--dof {3,6,9,12}	6	Degrees of freedom: 3=translation, 6=rigid, 9=rigid+scale, 12=affine.
--method {powell,gd}	powell	Registration backend: Powell by default, or legacy gradient descent.
--n_iters N	auto	Uniform optimisation iterations per executed pyramid level.
--level-iters A,B,...	—	Comma-separated coarse-to-fine per-level iteration schedule.
--lr LR	auto	Optimizer step size for the GD path.
--min-voxels N	16	Minimum pyramid level size.
--max-voxels N	full	Largest allowed dimension of the finest processed pyramid level.
--nosym	off	Disable symmetric halfway-space registration for the GD path.
--init-header	off	Use header alignment only.
--init-centroid	off	Initialize by aligning intensity centroids in RAS.
--init-center	default	Initialize by aligning geometric image centers in RAS.
--isotropic	off	Enable shared isotropic preprocessing before the GD pyramid.
--device DEVICE	cpu	Torch device string, e.g. cpu, cuda, mps, or gpu. Powell falls back to CPU.
--powell-brute-limit	30.0	Initial search half-width for the Powell brute-force stage.
--powell-brute-iters	1	Number of coarse-to-fine passes in the Powell brute-force stage.
--powell-brute-samples	30	Samples per dimension in the Powell brute-force stage.
--powell-maxiter	4	Maximum Powell iterations in the refinement stage.
--powell-sep	4	Sampling spacing for the Powell MRI_coreg-style evaluator.
--verbose	off	Enable INFO-level logging.
--debug	off	Enable DEBUG-level logging.

Example

coreg --mov T2.nii.gz --ref T1.mgz --out T2_to_T1.lta --dof 6 --verbose

---

bbreg — boundary-based registration

Registers a moving image to a T1 anatomy using cortical surface meshes (white matter / grey matter boundary). The tissue contrast direction (T1-like: WM > GM, or T2-like: GM > WM) is auto-detected from the image when not specified.

Surface input can be provided in three ways:

Mode A — FreeSurfer / FastSurfer subject directory (recommended)

bbreg --mov <moving.nii.gz> --subject_dir <subject_dir> --out <output.lta> [options]

The subject directory must contain surf/lh.white, surf/rh.white, and mri/orig.mgz (standard FreeSurfer / FastSurfer output layout).

Mode B — explicit surface files

bbreg --mov <moving.nii.gz> --lh_surf <lh.white> --rh_surf <rh.white> \
      --ref <T1.mgz> --out <output.lta> [options]

Mode C — segmentation file (no pre-built surfaces needed)

bbreg --mov <moving.nii.gz> --seg <aparc+aseg.mgz> --out <output.lta> [options]

The WM/GM boundary surface is extracted on-the-fly from a parcellation or aseg file (e.g. aparc+aseg.mgz, aseg.mgz, or NIfTI) via marching cubes. The segmentation header provides the reference geometry for the BBR stage. An optional --ref can still be supplied in this mode to drive the coarse Powell-based image NMI prealignment step.

Required arguments

Argument	Description
--mov FILE	Moving image to register (NIfTI or MGZ).
--out LTA	Output LTA file for the recovered transformation.
--subject_dir DIR	(Mode A) Subject directory with surfaces and mri/orig.mgz.
--lh_surf / --rh_surf FILE	(Mode B) Explicit left / right white surface files.
--ref FILE	(Mode B) Reference T1 image (required with explicit surfaces).
--seg FILE	(Mode C) Parcellation / aseg file; surfaces extracted automatically.

Options

Argument	Default	Description
--dof {6,9,12}	6	Degrees of freedom: 6=rigid, 9=rigid+scale, 12=affine.
--contrast {t1,t2}	auto	Tissue contrast: t1 (WM>GM) or t2 (GM>WM). Auto-detected if omitted.
--cost {contrast,gradient,both}	contrast	BBR cost term.
--wm_proj_abs MM	1.4	WM projection depth in mm.
--gm_proj_frac FRAC	0.5	GM projection as fraction of cortical thickness.
--slope SLOPE	0.5	Slope of the BBR sigmoid cost function.
--gradient_weight W	0.0	Weight for gradient cost term when --cost=both.
--n_iters N	200	Number of RMSprop optimisation iterations.
--lr LR	0.005	Optimiser learning rate.
--subsample N	2	Use every N-th surface vertex (1 = all).
--lh_thickness / --rh_thickness FILE	—	(Mode B) Cortical thickness files for GM projection.
--seg_smooth_sigma SIGMA	0.5	(Mode C) Gaussian pre-blur sigma (voxels) before marching cubes.
--seg_mc_level LEVEL	0.45	(Mode C) Marching-cubes iso-level.
--seg_smooth_iters N	50	(Mode C) Taubin smoothing iterations after marching cubes.
--init-lta FILE	—	Initialize from an existing LTA transform.
--init-header	off	Skip coarse NMI prealignment and rely on header geometry only.
--no-coreg-ref-mask	off	Disable the default aparc+aseg/aseg mask during coarse NMI prealignment.
--device DEVICE	cpu	Torch device string, e.g. cpu, cuda, mps, or gpu.
--verbose	off	Enable INFO-level logging.
--debug	off	Enable DEBUG-level logging.

Examples

# Mode A: register fMRI to T1 using a FastSurfer subject directory
bbreg --mov fMRI.nii.gz --subject_dir /data/subjects/sub-01 --out fMRI_to_T1.lta

# Mode B: register T2 to T1 with explicit surfaces and thickness files
bbreg --mov T2.nii.gz \
      --lh_surf /data/subjects/sub-01/surf/lh.white \
      --rh_surf /data/subjects/sub-01/surf/rh.white \
      --lh_thickness /data/subjects/sub-01/surf/lh.thickness \
      --rh_thickness /data/subjects/sub-01/surf/rh.thickness \
      --ref /data/subjects/sub-01/mri/orig.mgz \
      --out T2_to_T1.lta --contrast t2

# Mode C: register fMRI using a segmentation (no surface files required)
bbreg --mov fMRI.nii.gz --seg /data/subjects/sub-01/mri/aparc+aseg.mgz \
      --out fMRI_to_T1.lta

Run bbreg -h for a full argument summary with defaults.

---

segreg — segmentation-based registration

Registers a moving segmentation by aligning label centroids to another segmentation, to bundled atlas centroids such as fsaverage, or to a left-right flipped self target for upright/midspace workflows. The centroid fit now supports translation-only (3), rigid (6), similarity / global-scale (7), anisotropic no-shear scaling (9), or full affine (12) transforms.

segreg can also write mapped outputs while the package does not yet have a separate transform-application CLI. If --movimg is provided, --mapmov and --mapmovhdr map that intensity image with the recovered transform; otherwise those outputs map the moving segmentation itself.

segreg --mov <moving_seg.mgz> (--ref <ref_seg.mgz> | --ref-centroids <centroids.json> | --atlas fsaverage | --flipped) [options]

Common outputs

• --lta FILE writes the recovered transform as an LTA.
• --movimg FILE provides a separate intensity image for --mapmov or --mapmovhdr.
• --mapmov FILE writes a resliced mapped output. It maps --movimg when provided; otherwise it reslices the moving segmentation itself.
• --mapmovhdr FILE writes a header-only mapped output. It maps --movimg when provided; otherwise it remaps the moving segmentation itself.

Examples

# Register a subject segmentation to another segmentation
segreg --mov sub-01/aparc+aseg.mgz --ref sub-02/aparc+aseg.mgz \
       --lta sub01_to_sub02.lta

# Register a segmentation, then map a separate intensity image with the same transform
segreg --mov subj/mri/aparc+aseg.mgz --movimg subj/mri/orig.mgz --atlas fsaverage \
       --lta subj_to_fsaverage.lta --mapmov orig_in_fsaverage_space.mgz

# If --movimg is omitted, --mapmov maps the segmentation itself
segreg --mov subj/mri/aparc+aseg.mgz --atlas fsaverage \
       --mapmov aparc_aseg_in_fsaverage_space.mgz

# Export centroid JSON only
segreg --mov subj/mri/aparc+aseg.mgz --atlas fsaverage \
       --write-mov-centroids subj_centroids.json --write-ref-centroids fsaverage_centroids.json

Run segreg -h for a full argument summary with defaults.

---

lta — LTA and linear-transform utilities

Unified command for manipulating FreeSurfer-adjacent linear transforms with four subcommands: diff, invert, concat, and convert.

lta diff    LTA1 [LTA2] [options]                   # Compare transforms
lta invert  INPUT OUTPUT                            # Invert a transform
lta concat  LTA1 LTA2 OUTPUT                        # Concatenate two transforms
lta convert INPUT OUTPUT [--src-img SRC --dst-img DST]  # Convert formats

---

lta diff — Compare transforms

Computes distance metrics between two LTA transforms, or between a single transform and identity. Replicates the functionality of FreeSurfer's lta_diff.

Usage

lta diff LTA1 [LTA2] [--dist {1,2,3,4,5,7}] [--radius MM] [--normdiv FLOAT]
                     [--invert1] [--invert2]

Distance types

--dist	Metric
1	Rigid transform distance: √(‖log R_d‖² + ‖T_d‖²)
2	Affine RMS distance (Jenkinson 1999) — default
3	Mean displacement at the 8 corners of the source volume (mm)
4	Max displacement on a sphere of radius r
5	Determinant of M1 (or M1·M2 when two transforms are given)
7	Polar decomposition: rotation, shear, scales, translation, determinant

Options

Argument	Default	Description
--dist {1,2,3,4,5,7}	2	Distance type (see table above).
--radius MM	100	Sphere / RMS radius in mm (used by dist 2 and 4).
--normdiv FLOAT	1	Divide the final distance by this value (must be > 0).
--invert1	off	Invert LTA1 before comparison.
--invert2	off	Invert LTA2 before comparison (requires a second LTA).

Examples

# Affine RMS distance between two registrations (default metric)
lta diff myreg.lta ref.lta

# Rigid distance
lta diff myreg.lta ref.lta --dist 1

# Polar decomposition of a single transform
lta diff myreg.lta --dist 7

---

lta invert — Invert a transform

Inverts an LTA transform and writes the result to a new file. The output is always stored as LINEAR_RAS_TO_RAS with src and dst geometry swapped.

Usage

lta invert INPUT OUTPUT

Examples

# Invert a registration
lta invert T1_to_MNI.lta MNI_to_T1.lta

# Invert twice to verify round-trip
lta invert fwd.lta inv.lta
lta invert inv.lta fwd_again.lta

---

lta concat — Concatenate transforms

Concatenates two LTA transforms. If LTA1 maps A→B and LTA2 maps B→C, the output maps A→C. Equivalent to FreeSurfer's mri_concatenate_lta.

Usage

lta concat LTA1 LTA2 OUTPUT

Examples

# Concatenate fMRI→T1 and T1→MNI to get fMRI→MNI
lta concat fMRI_to_T1.lta T1_to_MNI.lta fMRI_to_MNI.lta

# Chain multiple registrations
lta concat moving_to_intermediate.lta intermediate_to_fixed.lta moving_to_fixed.lta

---

lta convert — Convert between transform formats

Converts between .lta, .xfm, volumetric tkregister .dat/.reg, FSL .mat/.fslmat, ITK/ANTs 3D affine text transforms, experimental ANTs Matlab-format affine transforms, experimental AFNI affine text transforms, and NiftyReg affine text matrices by normalizing through an internal RAS-to-RAS LTA.

Usage

lta convert INPUT OUTPUT [--in-format {lta,xfm,fsl,regdat,itk,antsmat,afni,niftyreg}]
                         [--out-format {lta,xfm,fsl,regdat,itk,antsmat,afni,niftyreg}]
                         [--src-img SRC] [--dst-img DST]
                         [--out-type {ras2ras,vox2vox}]
                         [--subject SUBJECT] [--fscale FSCALE]
                         [--float2int {tkregister,round,floor}]

Supported formats

• .lta — FreeSurfer Linear Transform Array
• .xfm — MNI/MINC linear transform
• .dat — old tkregister volumetric register.dat format
• .mat / .fslmat — FSL FLIRT affine matrix
• .tfm — ITK/ANTs 3D affine text transform
• *GenericAffine.mat — experimental ANTs / ITK Matlab-format affine transform
• .aff12.1D — experimental AFNI affine text matrix
• .niftyreg.txt — NiftyReg 3D affine text matrix

Notes

• Reading register.dat requires both --src-img and --dst-img, because the stored matrix is defined in tkregister coordinates rather than scanner RAS.
• Reading FSL .mat / .fslmat also requires both --src-img and --dst-img, because the stored matrix is defined in FSL voxel conventions rather than scanner RAS.
• Reading .xfm, ITK/ANTs text affines, experimental ANTs .mat, experimental AFNI affine text, or NiftyReg text matrices without images still preserves the transform matrix, but the resulting LTA geometry blocks stay marked as valid=0 unless you provide --src-img and --dst-img.
• Use --in-format / --out-format for ambiguous filenames such as .txt, .1D, or .mat.
• ITK/ANTs text support currently targets 3D affine transforms only, not binary or composite transform files.
• Experimental ANTs .mat support is implemented via SciPy using ITK Matlab IO semantics.
• Experimental AFNI support targets affine text matrices in AFNI's DICOM/LPS convention.
• NiftyReg stores the inverse target-to-source RAS matrix in the file, matching FreeSurfer's lta_convert handling.
• --subject, --fscale, and --float2int apply when writing register.dat.
• --out-type applies when writing .lta output.

Examples

# XFM -> LTA with explicit image geometry
lta convert talairach.xfm talairach.lta --src-img mov.mgz --dst-img ref.mgz

# LTA -> XFM
lta convert sub01_to_mni.lta sub01_to_mni.xfm

# LTA -> tkregister register.dat
lta convert bold_to_orig.lta bold_to_orig.dat --subject sub-01 --fscale 0.1

# tkregister register.dat -> LTA
lta convert bold_to_orig.dat bold_to_orig.lta --src-img bold.nii.gz --dst-img orig.mgz

# LTA -> FSL FLIRT matrix
lta convert bold_to_orig.lta bold_to_orig.mat

# FSL FLIRT matrix -> LTA
lta convert bold_to_orig.mat bold_to_orig_from_fsl.lta --src-img bold.nii.gz --dst-img orig.mgz

# LTA -> ITK/ANTs text affine
lta convert bold_to_orig.lta bold_to_orig.tfm

# ITK/ANTs text affine (.txt requires explicit format override)
lta convert bold_to_orig.txt bold_to_orig_from_itk.lta --in-format itk --src-img bold.nii.gz --dst-img orig.mgz

# LTA -> experimental ANTs GenericAffine .mat
lta convert bold_to_orig.lta bold_to_orig0GenericAffine.mat

# Experimental ANTs GenericAffine .mat -> LTA (.mat is otherwise assumed FSL)
lta convert bold_to_orig0GenericAffine.mat bold_to_orig_from_antsmat.lta
lta convert some_affine.mat bold_to_orig_from_antsmat.lta --in-format antsmat

# LTA -> experimental AFNI affine text
lta convert bold_to_orig.lta bold_to_orig.aff12.1D

# Experimental AFNI affine text -> LTA (.1D may need explicit format override)
lta convert bold_to_orig.aff12.1D bold_to_orig_from_afni.lta

# LTA -> NiftyReg affine text matrix (.txt requires explicit format override)
lta convert bold_to_orig.lta bold_to_orig.txt --out-format niftyreg

# NiftyReg affine text matrix -> LTA
lta convert bold_to_orig.txt bold_to_orig_from_niftyreg.lta --in-format niftyreg

General notes

• All metrics are numerically matched to FreeSurfer's lta_diff (except for a known bug in the C++ single-transform corner metric, which is fixed here).
• When only one LTA is supplied to diff, the transform is compared against identity.
• Run lta --help or lta <subcommand> --help for detailed usage.

Python API

import nibabel as nib

from neuroreg import bbreg, coreg, robreg

# Public robust image-to-image registration (IRLS-backed).
# Intended for same-/similar-contrast pairs; symmetric mode is the default.
# On Apple MPS, this path currently warns and falls back to CPU.
transform_robreg = robreg("T1_repeat.nii.gz", "T1_baseline.mgz")

# The same public robreg path with pre-loaded nibabel images.
mov_img = nib.load("T1_repeat.nii.gz")
ref_img = nib.load("T1_baseline.mgz")
transform_robreg_loaded = robreg(mov_img, ref_img)

# Image-based cross-modal registration path for cases where no
# white-matter surface or segmentation is available. By default this
# uses the Powell-based MRI_coreg-style path.
transform_coreg = coreg("T2.nii.gz", "T1.mgz")

# Surface-based (BBR) registration — Mode A: subject directory
transform_bbreg = bbreg(
    mov="fMRI.nii.gz",
    subject_dir="/data/subjects/sub-01",
    lta_name="fMRI_to_T1.lta",
    dof=6,
)

# Surface-based (BBR) registration — Mode B: explicit surface files
transform_bbreg = bbreg(
    mov="T2.nii.gz",
    lh_surf="/data/subjects/sub-01/surf/lh.white",
    rh_surf="/data/subjects/sub-01/surf/rh.white",
    lh_thickness="/data/subjects/sub-01/surf/lh.thickness",
    rh_thickness="/data/subjects/sub-01/surf/rh.thickness",
    ref="/data/subjects/sub-01/mri/orig.mgz",
    lta_name="T2_to_T1.lta",
    contrast="t2",
)

# Surface-based (BBR) registration — Mode C: segmentation (no surface files needed)
transform_bbreg = bbreg(
    mov="fMRI.nii.gz",
    seg="/data/subjects/sub-01/mri/aparc+aseg.mgz",
    lta_name="fMRI_to_T1.lta",
)

# For debugging or inspection, request the fitted model explicitly.
transform_bbreg, model_bbreg = bbreg(
    mov="fMRI.nii.gz",
    subject_dir="/data/subjects/sub-01",
    return_model=True,
)

Degrees of freedom

Current DOF support is:

Command / API path	Supported DOF
robreg / public robreg()	6 only
coreg / public coreg()	3, 6, 9, 12
bbreg / public bbreg()	6, 9, 12
segreg / public segreg()	3, 6, 7, 9, 12

The public robreg path is intentionally rigid-only for now because it tracks the current IRLS implementation.

Also note that the current public robreg path is aimed at same-/similar-contrast registration. For cross-sequence alignment such as T2→T1, the current options are coreg (Powell-based by default, with legacy GD still available via method="gd") or bbreg when a segmentation or surfaces are available.

API Documentation

The API Documentation can be found at https://deep-mi.org/neuroreg.

References

If you use this software for a publication please cite:

• Reuter, Rosas, Fischl (2010). Highly accurate inverse consistent registration: a robust approach. NeuroImage 53(4):1181-1196. https://doi.org/10.1016/j.neuroimage.2010.07.040

• Reuter, Schmansky, Rosas, Fischl (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61(4):1402-1418. https://doi.org/10.1016/j.neuroimage.2012.02.084

• Reuter, Fischl (2011). Avoiding asymmetry-induced bias in longitudinal image processing. NeuroImage 57(1):19-21. https://doi.org/10.1016/j.neuroimage.2011.02.076

If you use bbreg specifically, please also cite:

• Greve, Fischl (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48(1):63-72. https://doi.org/10.1016/j.neuroimage.2009.06.060

We invite you to check out our lab webpage at https://deep-mi.org