1 citations · 1 across the 14 of their papers we have counts for
14 papers
Uncertainty-Aware 3D Residual Wavelet Diffusion for Ultra Low-Field MRI Super-Resolution
Rui W. Yeow, Millie Beament, Fred Dick +6
Ultra low-field MRI expands global access to neuroimaging but produces scans with low signal-to-noise ratio, reduced contrast, and thick slices. While regression-based super-resolu…
BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis
Moona Mazher, Abdul Qayyum, Steven A. Niederer +1
Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data. However, exis…
Bridging Single Distortion Artifacts and Multifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks
Yucheng Tang, Alexander Ng, Wen Yan +11
Clinical prostate multi-parametric MRI relies heavily on high-quality diffusion-weighted imaging (DWI), yet reading DWI is frequently compromised by geometric distortion, often cau…
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…
Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction
YuCheng Tang, Wen Yan, Alexander Ng +13
Single-shot echo-planar prostate diffusion-weighted imaging (DWI) is frequently complicated by geometric distortions, which impact the ability to derive reliable diagnoses from suc…
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
John Kalkhof, Boris Gutman, Emile d'Angremont +2
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical images. Existing appro…
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…
ProFound: A moderate-sized vision foundation model for multi-task prostate imaging
Yipei Wang, Yinsong Xu, Weixi Yi +11
Many diagnostic and therapeutic clinical tasks for prostate cancer increasingly rely on multi-parametric MRI. Automating these tasks is challenging because they necessitate expert…
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…
Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis
Junkai Liu, Nay Aung, Theodoros N. Arvanitis +3
Missing data problems, such as missing modalities in multi-modal brain MRI and missing slices in cardiac MRI, pose significant challenges in clinical practice. Existing methods rel…
Towards Generalisable Foundation Models for Brain MRI
Moona Mazher, Geoff J. M. Parker, Daniel C. Alexander
Foundation models in artificial intelligence (AI) are transforming medical imaging by enabling general-purpose feature learning from large-scale, unlabeled datasets. In this work,…
Deep generative priors for 3D brain analysis
Ana Lawry Aguila, Dina Zemlyanker, You Cheng +6
Diffusion models have recently emerged as powerful generative models in medical imaging. However, it remains a major challenge to combine these data-driven models with domain knowl…
A Modality-agnostic Multi-task Foundation Model for Human Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +5
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +2
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…