normaltwinpet
Generate a whole-body normal twin PET from a CT image, using the CT-only model from:
Hinge et al., Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT, Scientific Reports 15, 42662 (2025). https://doi.org/10.1038/s41598-025-26827-y
Input is a CT, output is the normal twin PET, on the same voxel grid as the CT.
The output is normalized as image-derived lean-body-mass SUV (see ID-uptake-values).
Usage
pip install normaltwinpet
normaltwinpet -i ct.nii.gz -o ntpet.nii.gz
Uses CUDA if available, otherwise CPU (slow). Override with --device.
On first run the weights (~250 MB) are downloaded from the GitHub release and
cached in the user cache directory (~/.cache/normaltwinpet on Linux). Set
NORMALTWINPET_HOME to use another location.
Notes
The CT is resampled to 2 mm and cropped/padded to 256x256 in-plane for the network, then resampled back to the input grid. Anatomy outside the central 256x256 (512 mm) in-plane field of view is not predicted and will be zero.
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Metadata
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Release files for normaltwinpet 0.1.1
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