9 papers
Learning Cardiac Motion Priors for Implicit Neural Representations
Andrew Bell, George Webber, Andrew P King +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…
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…
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…