cocolit 0.2.1

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis

CoCoLIT (AAAI-26)

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis
Alec Sargood, Lemuel Puglisi, James Cole, Neil Oxtoby, Daniele Ravì^†, Daniel C. Alexander^†
* Joint first authors, † Joint senior authors

Installation • Usage • Training & Reproducibility • Disclaimer • Citing

Installation

This repository requires Python 3.10 and PyTorch 2.0 or later. To install the latest version, run:

pip install cocolit

Usage

After installing the package, you can convert a T1-weighted MRI to a Florbetapir SUVR map by running:

mri2pet --i /path/to/t1.nii.gz --o /path/to/output.nii.gz

To replicate the results presented in the paper, include the --m 64 flag.

Training & Reproducibility

To reproduce the experiments reported in the paper, please follow the reproducibility guide.

Disclaimer

This software is not intended for clinical use. The code is not available for commercial applications. For commercial inquiries, please contact the corresponding authors.

Citing

Arxiv Preprint:

@article{sargood2025cocolit,
  title={CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis},
  author={Sargood, Alec and Puglisi, Lemuel and Cole, James H and Oxtoby, Neil P and Rav{\`\i}, Daniele and Alexander, Daniel C},
  journal={arXiv preprint arXiv:2508.01292},
  year={2025}
}