1 citations · 1 across the 5 of their papers we have counts for
5 papers
Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution
Mark D. Olchanyi, Annabel Sorby-Adams, John Kirsch +7
Portable, ultra-low-field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatial and angular resolutions and…
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…
From Low Field to High Value: Robust Cortical Mapping from Low-Field MRI
Karthik Gopinath, Annabel Sorby-Adams, Jonathan W. Ramirez +12
Three-dimensional reconstruction of cortical surfaces from MRI for morphometric analysis is fundamental for understanding brain structure. While high-field MRI (HF-MRI) is standard…
PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Peirong Liu, Oula Puonti, Annabel Sorby-Adams +2
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR p…