7 citations · 9 across the 9 of their papers we have counts for
11 papers
One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control
Riccardo Barbano, Vincent Pauline, Runchang Li +6
Deep generative models often produce structured outputs composed of interacting components. Modelling these outputs with a single model requires learning both the component distrib…
Generative Priors for Total-Body PET Reconstruction
Movindu Dassanayake, George Webber, Denis Prokopenko +2
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…
Learning Cardiac Motion Priors for Implicit Neural Representations
Andrew Bell, George Webber, Steffen E Petersen +3
Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields. However, fitting an INR to each…
Solving Inverse Problems with Flow-based Models via Model Predictive Control
George Webber, Alexander Denker, Riccardo Barbano +1
Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging. Recent work casts…
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…
Distributional Consistency Loss: Beyond Pointwise Data Terms in Inverse Problems
George Webber, Andrew J. Reader
Recovering true signals from noisy measurements is a central challenge in inverse problems spanning medical imaging, geophysics, and signal processing. Current methods balance prio…
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…
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…
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…
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…