6 papers · 1 filter
Steerable Conditional Diffusion for Domain Adaptation in PET Image Reconstruction
George Webber, Alexander Hammers, Andrew P. King +1
Diffusion models have recently enabled state-of-the-art reconstruction of positron emission tomography (PET) images while requiring only image training data. However, domain shift…
Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction
George Webber, Alexander Hammers, Andrew P. King +1
Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparameters (e.g. scanner geometry or dose level) when PET images are reconstructed by lev…
Supervised Diffusion-Model-Based PET Image Reconstruction
George Webber, Alexander Hammers, Andrew P King +1
Diffusion models (DMs) have recently been introduced as a regularizing prior for PET image reconstruction, integrating DMs trained on high-quality PET images with unsupervised sche…
Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction
George Webber, Yuya Mizuno, Oliver D. Howes +3
Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, includin…
Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction
George Webber, Yuya Mizuno, Oliver D. Howes +3
Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image qu…
Generative-Model-Based Fully 3D PET Image Reconstruction by Conditional Diffusion Sampling
George Webber, Yuya Mizuno, Oliver D. Howes +3
Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) datasets. In this work we have d…