42 citations · 59 across the 14 of their papers we have counts for
14 papers
Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
Ahmed Karam Eldaly, Matteo Figini, Daniel C. Alexander
We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imaging data. The problem is formulat…
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…
MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo +2
Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness. In many clinical workflows, co…
HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restoration
Seunghoi Kim, Henry F. J. Tregidgo, Chen Jin +2
Generative models are prone to hallucinations: plausible but incorrect structures absent in the ground truth. This issue is problematic in image restoration for safety-critical dom…
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…
Tackling Structural Hallucination in Image Translation with Local Diffusion
Seunghoi Kim, Chen Jin, Tom Diethe +5
Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in…
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…
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Hongxiang Lin, Matteo Figini, Felice D'Arco +10
Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher…
Deformably-Scaled Transposed Convolution
Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu +3
Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers. In this work we revisit transposed convo…
Progressive Subsampling for Oversampled Data - Application to Quantitative MRI
Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu +3
We present PROSUB: PROgressive SUBsampling, a deep learning based, automated methodology that subsamples an oversampled data set (e.g. multi-channeled 3D images) with minimal loss…
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…
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10
MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…