petprep

Preprocessing of positron emission tomography (PET) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. PETPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for PET data. PETPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. PETPrep robustly produces high-quality results on diverse PET data. Additionally, PETPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. PETPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

The workflow is based on Nipype and encompasses a large set of tools from well-known neuroimaging packages, including FSL, ANTs, FreeSurfer, AFNI, and Nilearn. This pipeline was designed to provide the best software implementation for each state of preprocessing, and will be updated as newer and better neuroimaging software becomes available.

PETPrep performs basic preprocessing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skullstripping etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state PET imaging, graph theory measures, surface or volume-based statistics, etc. PETPrep allows you to easily do the following:

Take PET data from unprocessed (only reconstructed) to ready for analysis.

Implement tools from different software packages.

Achieve optimal data processing quality by using the best tools available.

Generate preprocessing-assessment reports, with which the user can easily identify problems.

Receive verbose output concerning the stage of preprocessing for each subject, including meaningful errors.

Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.

[Nat Meth doi:10.1038/s41592-018-0235-4] [Documentation petprep.org] [Software doi:10.5281/zenodo.852659] [Support neurostars.org]

License information

PETPrep adheres to the general licensing guidelines of the NiPreps framework.

License

As of the 21.0.x pre-release and release series, PETPrep is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

petprep-0.0.10.tar.gz (28.8 MB view details)

Uploaded Aug 11, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

petprep-0.0.10-py3-none-any.whl (3.5 MB view details)

Uploaded Aug 11, 2026 Python 3

File details

Details for the file petprep-0.0.10.tar.gz.

File metadata

Download URL: petprep-0.0.10.tar.gz
Upload date: Aug 11, 2026
Size: 28.8 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for petprep-0.0.10.tar.gz
Algorithm	Hash digest
SHA256	`21676baa60a4bd5c413edd07d49af0976e097cfce6c707d962e7bb3d1e3f6776`
MD5	`46cca879cf7f53d82ba72c6ea0bad670`
BLAKE2b-256	`2e0bb684e9690cb4844dacd73b455a27f7b5f8a5e6bbfbe87ef020e2c8426ab4`

See more details on using hashes here.

Provenance

The following attestation bundles were made for petprep-0.0.10.tar.gz:

Publisher: build-test-deploy.yml on nipreps/petprep

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: petprep-0.0.10.tar.gz
- Subject digest: 21676baa60a4bd5c413edd07d49af0976e097cfce6c707d962e7bb3d1e3f6776
- Sigstore transparency entry: 2424350734
- Sigstore integration time: Aug 11, 2026
Source repository:
- Permalink: nipreps/petprep@021cd6d19369e0f5b8e2cdfc924eb7f37f7f0e07
- Branch / Tag: refs/tags/0.0.10
- Owner: https://github.com/nipreps
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: build-test-deploy.yml@021cd6d19369e0f5b8e2cdfc924eb7f37f7f0e07
- Trigger Event: push