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arXiv · 2610.23870

Generative Priors for Total-Body PET Reconstruction

Movindu Dassanayake·George Webber·Denis Prokopenko·Julia A. Schnabel·Andrew J. Reader
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Abstract

Total-body positron emission tomography (TB-PET) enables fast, low-dose, and dynamic imaging over a long axial field of view, but reconstruction remains challenging due to reduced counts, large image volumes, and increased data dimensionality. Generative models offer powerful learned priors for low-count PET, yet explicit PET measurement conditioning at TB scale has not been demonstrated. Inspired by PET decomposed diffusion sampling (PET-DDS), we propose TB-PET-DDS, a reconstruction framework that combines a CT-conditioned generative prior with patient-specific list-mode PET data. During reverse diffusion, PET consistency is enforced using a closed-form 3D TB-PET maximum-a-posteriori expectation maximisation fusion update, rather than relying solely on image-domain conditioning. The synthetic measurements are generated from the low-dose vendor reconstruction, providing a controlled inverse problem and retaining a full-dose reference. Proof-of-concept evaluation was performed on a single clinical TB-PET acquisition, with reconstruction hyperparameters selected against the full-dose reconstruction from the same case for all models. TB-PET-DDS improved NRMSE and SSIM relative to the DDIM and Image-DDS baselines, improving through-plane consistency in coronal views while enhancing agreement with the measured PET data. Although smoothed low-dose vendor reconstructions achieved the best global voxel-wise metrics, TB-PET-DDS produced the lowest liver coefficient of variation. These results demonstrate the feasibility of measurement conditioned diffusion reconstruction at TB scale and provide an initial route towards learned, projector-consistent generative reconstruction for low-count TB-PET imaging.

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Movindu Dassanayake, George Webber, Denis Prokopenko, Julia A. Schnabel, Andrew J. Reader. 2026-09-18. Generative Priors for Total-Body PET Reconstruction. https://arxiv.org/abs/2610.23870

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