2 citations · 4 across the 5 of their papers we have counts for
7 papers · 1 filter
Bayesian Uncertainty-Aware MRI Reconstruction
Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander
We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as…
Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS
Seunghoi Kim, Henry F. J. Tregidgo, Matteo Figini +3
Hallucinations are spurious structures not present in the ground truth, posing a critical challenge in medical image reconstruction, especially for data-driven conditional models.…
Alternative Learning Paradigms for Image Quality Transfer
Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander
Image Quality Transfer (IQT) aims to enhance the contrast and resolution of low-quality medical images, e.g. obtained from low-power devices, with rich information learned from hig…
Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI
Alp G. Cicimen, Henry F. J. Tregidgo, Matteo Figini +7
Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultr…
A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI
Seunghoi Kim, Henry F. J. Tregidgo, Ahmed K. Eldaly +2
Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply. However, they often yield images with lower spatial resolution…
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
Matteo Figini, Hongxiang Lin, Godwin Ogbole +10
1.5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robu…