Python 3.9 | 3.10 | 3.11 | 3.12 License: Apache 2.0 Release

Note: This project is still in development and may contain bugs. Please report issues if you encounter problems.

T1Prep: T1 PREProcessing Pipeline (aka PyCAT)

T1Prep is a Python pipeline for preprocessing and segmenting T1-weighted MRI data. It supports:

  • Bias-field correction and denoising
  • Brain extraction (skull stripping)
  • Tissue segmentation (GM, WM, CSF)
  • Cortical surface reconstruction and thickness estimation
  • Non-linear spatial registration to MNI152 space
  • Atlas-based ROI extraction
  • White matter hyperintensity (WMH/lesion) detection
  • BIDS derivatives output naming

Cortical surface reconstruction uses the cat-surf Python package (pure Python bindings to the CAT-Surface C library — no compiled binaries required).

For full documentation, CLI usage, Docker instructions, and helper scripts see the GitHub repository.

Installation

pip install T1Prep

pip install places every entry point into the active environment's bin/ directory. With that directory on your PATH (e.g. an activated venv) the following commands are available:

Command Role
T1Prep main CLI — batch + parallel processing (--multi)
t1prep-ui browser-based web UI
t1prep-run single-subject Python entry
CAT_SurfView surface viewer
t1prep-download-models fetch model weights

Download model weights

Model weights are not bundled in the wheel (they are ~500 MB). Download them after installation:

t1prep-download-models

Models are stored alongside the deepmriprep package data and are downloaded automatically on first pipeline use if this step is skipped.

Requirements

  • Python 3.9–3.12
  • ~2 GB disk space for model weights (downloaded separately, see above)
  • For GPU acceleration: CUDA-capable GPU or Apple Silicon (MPS)

Python API

from t1prep import run_t1prep

# Single file — results saved next to input
run_t1prep("/data/sub-01_T1w.nii.gz")

# Single file, BIDS-compatible output
run_t1prep("/data/sub-01/ses-1/anat/sub-01_ses-1_T1w.nii.gz", bids=True)

# Batch processing with options
run_t1prep(
    ["/data/T1/sub-01.nii.gz", "/data/T1/sub-02.nii.gz"],
    out_dir="/results",
    atlas=["neuromorphometrics", "suit"],
    multi=-1,          # auto-detect parallelism
    wp=True,           # save warped segmentations
    p=True,            # save native segmentations
    csf=True,          # save CSF segmentation
    lesions=True,      # save WMH lesion map
    gz=True,           # compress outputs (.nii.gz)
    stream_output=True,
    log_file="/results/T1Prep_run.log",
)

Key parameters

Parameter Type Description
files str or list[str] Input NIfTI file(s)
out_dir str Output directory (default: same as input)
atlas list[str] Atlas names for ROI extraction
surf bool Run cortical surface estimation (default: True)
multi int Parallel workers; -1 = auto (default: 1)
bids bool Use BIDS derivatives naming
gz bool Save compressed NIfTI (.nii.gz)
wp bool Save warped (MNI space) segmentations
p bool Save native space segmentations
csf bool Save CSF segmentation
lesions bool Save WMH/lesion map
amap bool Use AMAP segmentation (CAT12-style)
skullstrip_only bool Only run skull stripping then exit
skip_skullstrip bool Skip skull stripping (pre-stripped input)

Output structure

Non-BIDS (default): subfolders mri/, surf/, report/, label/ in the output directory, with CAT12-compatible filenames (e.g., mwp1sub-01.nii, lh.thickness.sub-01).

BIDS (with bids=True): BIDS derivatives layout <out_dir>/derivatives/T1Prep-v<version>/sub-XX/ses-YY/anat/.

License

Distributed under the Apache License 2.0.

Project links

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  • Released:
Newer release available (0.5.5)

1 maintainer

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Avatar for ChristianGaser from gravatar.com ChristianGaser

Credits

Author: Christian Gaser

GitHub Statistics

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License

Apache Software License (Apache License)

Requires

Python <3.13, >=3.9

Provides Extra

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