PETRIC 2: Second PET Rapid Image reconstruction Challenge

Welcome to PETRIC 2!

(If you have not heard about PETRIC before, you can find out about it here.)

What's the same?

We are going to solve a maximum a-posteriori estimate (MAP) using the smoothed relative difference prior (RDP) reconstruction problem and the aim of the challenge is to reach the target image quality as fast as possible. We will provide you with PET sinogram phantom data from different scanners and an example repository on GitHub with an implementation of a reference algorithm. There will be a leaderboard which is continuously updated to make sure you know how you are doing.

What's new?

This time we are going to make things more challenging! The PET sinogram data will be created from fewer counts which means your algorithm will have to cope with more noise in the data. For more information on the new data please go to wiki/data.

In addition to the more challenging data, we have improved our reconstruction software. STIR 6.3 was released which has lots of new features including new analytic reconstruction methods, better GPU support and improved support for reading raw data formats. For more information have a look at the release notes. On the SIRF side we focused on speed! We improved the acquisition and image algebra to speed up things by a factor of 3 and optimised our Python interface to ensure we provide data views rather than copying things around. Have a look at the SIRF 3.9 relase notes for more information.

Timeline

• Start of the challenge: 15 November 2025
• End of the challenge: 15 February 2026

Awards

The winners of PETRIC2 will be announced as part of the Symposium on AI & Reconstruction for Biomedical Imaging taking place from 9 – 10 March 2026 in London (https://www.ccpsynerbi.ac.uk/events/airbi/). All participants of PETRIC2 will be invited to submit an abstract at the beginning of December 2025 and will then have the opportunity to present their work at the Symposium. More information on the abstract and possible travel stipends will follow soon.

Layout

The organisers will import your submitted algorithm from main.py and then run & evaluate it. Please create this file! See the example main_*.py files for inspiration.

SIRF, CIL, and CUDA are already installed (using synerbi/sirf). Additional dependencies may be specified via apt.txt, environment.yml, and/or requirements.txt.

• (required) main.py: must define a class Submission(cil.optimisation.algorithms.Algorithm) and a (potentially empty) list of submission_callbacks, e.g.:

  • main_BSREM.py
  • main_ISTA.py
  • main_OSEM.py

• apt.txt: passed to apt install

• environment.yml: passed to conda install, e.g.:

  name: winning-submission
  channels: [conda-forge, nvidia]
  dependencies:
  - cupy
  - cuda-version 12.8.*
  - pip
  - pip:
    - git+https://github.com/MyResearchGroup/prize-winning-algos

• requirements.txt: passed to pip install, e.g.:

  cupy-cuda12x
  git+https://github.com/MyResearchGroup/prize-winning-algos

Tip

You probably should create either an environment.yml or requirements.txt file (but not both).

You can also find some example notebooks here which should help you with your development:

• https://github.com/SyneRBI/SIRF-Contribs/blob/master/src/notebooks/BSREM_illustration.ipynb

Organiser Setup

The organisers will execute (after installing nvidia-docker & downloading https://petric2.tomography.stfc.ac.uk/data/ to /path/to/data):

TODO: start petric2 data server

TODO: use synerbi/sirf:latest-gpu after the next SIRF release

# 1. git clone & cd to your submission repository
# 2. mount `.` to container `/workdir`:
docker run --rm -it --gpus all -p 6006:6006 \
  -v /path/to/data:/mnt/share/petric:ro \
  -v .:/workdir -w /workdir synerbi/sirf:edge-gpu /bin/bash
# 3. install metrics & GPU libraries
conda install -y tensorboard tensorboardx jupytext
pip install git+https://github.com/Project-MONAI/[email protected] torch tensorflow[and-cuda]==2.20 --extra-index-url https://download.pytorch.org/whl/cu128
pip install git+https://github.com/TomographicImaging/Hackathon-000-Stochastic-QualityMetrics
# 4. optionally, conda/pip/apt install environment.yml/requirements.txt/apt.txt
# 5. run your submission
python petric.py &
# 6. optionally, serve logs at <http://localhost:6006>
tensorboard --bind_all --port 6006 --logdir ./output

FAQ

See the wiki/Home and wiki/FAQ for more info.

Tip

petric.py will effectively execute:

from main import Submission, submission_callbacks  # your submission (`main.py`)
from petric import data, metrics  # our data & evaluation
assert issubclass(Submission, cil.optimisation.algorithms.Algorithm)
Submission(data).run(numpy.inf, callbacks=metrics + submission_callbacks)

---

Warning

To avoid timing out (currently 10 min runtime, will likely be increased a bit for the final evaluation after submissions close), please disable any debugging/plotting code before submitting! This includes removing any progress/logging from submission_callbacks and any debugging from Submission.__init__.

• data to test/train your Algorithms is available at https://petric2.tomography.stfc.ac.uk/data/ and is likely to grow (more info to follow soon)
  • fewer datasets will be available during the submission phase, but more will be available for the final evaluation after submissions close
  • please contact us if you'd like to contribute your own public datasets!
• metrics are calculated by class QualityMetrics within petric.py
  • this does not contribute to your runtime limit
  • effectively, only Submission(data).run(np.inf, callbacks=submission_callbacks) is timed
• when using the temporary [leaderboard], it is best to:
  • change Horizontal Axis to Relative
  • untick Ignore outliers in chart scaling
  • see the wiki for details

Any modifications to petric.py are ignored.

[leaderboard]: Will be set up soon!